{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}mainlog
       {txt}log:  {res}D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\code\estimation\basics_rel_demand\logs/5_demand_estimation.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 9 Jul 2025, 17:15:57
{txt}
{com}. 
. *Define global specification variables: 
. 
. *run time vs goods estimation if 1
. global est_time_goods=1
{txt}
{com}. 
. *run chcare vs goods estimation if 1
. global est_chcare_goods=1
{txt}
{com}. 
. 
. 
. ******************************************************************
. **   A. Download child panel with reasonably aged parents*********
. ******************************************************************
. 
. clear 
{txt}
{com}. use "$data/psid_fam.dta", replace
{txt}
{com}. 
. 
. ******************************************************************
. ***********    B. Sample restrictions ***************
. ******************************************************************
. 
. * restrict to those with some measure of investment in CDS_invest_ind
. keep if CDS_invest_ind==1
{txt}(25,600 observations deleted)

{com}. 
. ***********    Keep kids ages 5-12 only    ***************
. 
. keep if age>=5 & age<=12
{txt}(3,277 observations deleted)

{com}. 
. ************ Families with only 1 or 2 children ages<=12 ************
. 
. keep if num_0_12<=2
{txt}(888 observations deleted)

{com}. 
. *************** Only include CDS years 1997, 2002, 2007 ****************
. 
. * keep if year==1997 | year==2002 | year==2007
. 
. ****** ONLY KEEP LOG WAGE FE VALUES IF AT LEAST 3 OBSERVATIONS USED *******
. 
. local N_min_FE = 5     // Minimum number of observed wages to keep log wage FE
{txt}
{com}. 
. replace ln_wageFE_m  =. if N_FE_m< `N_min_FE' 
{txt}(537 real changes made, 537 to missing)

{com}. replace ln_wageFE_f  =. if N_FE_f< `N_min_FE'  
{txt}(351 real changes made, 351 to missing)

{com}. 
. 
. *******************************************************************************************************
. *************************************   C. LOCAL LISTS OF REGRESSORS  *********************************
. *******************************************************************************************************
. 
. local medvars    "med_hs med_scoll med_collplus"
{txt}
{com}. local fedvars    "fed_hs fed_scoll fed_collplus"
{txt}
{com}. local fvars      "fed_hs fed_scoll fed_collplus f_age"
{txt}
{com}. local famvars    "m_white num_0_5 num_child"
{txt}
{com}. 
. 
. local p_work_min_m= .75               // to limit sample to those with a minimum probability of mother working
{txt}
{com}. local p_work_min_f= .90               // to limit sample to those with a minimum probability of father working
{txt}
{com}. 
. 
. local Zm1 = " "
{txt}
{com}. local Zm2 = " `medvars' "
{txt}
{com}. local Zm3 = " age `medvars' m_age"
{txt}
{com}. local Zm4 = " age `medvars' m_age `famvars' "
{txt}
{com}. local Zm5 = " age med_scoll med_collplus m_age `famvars' "
{txt}
{com}. local Zm6 = " age med_scoll med_collplus m_age `famvars' m_pc97 "
{txt}
{com}. 
. local Zf1 = " "
{txt}
{com}. local Zf2 = " `fedvars' "
{txt}
{com}. local Zf3 = " age `fedvars' f_age"
{txt}
{com}. local Zf4 = " age `fedvars' f_age `famvars' "
{txt}
{com}. local Zf5 = " age fed_scoll fed_collplus f_age `famvars' "
{txt}
{com}. local Zf6 = " age fed_scoll fed_collplus f_age `famvars' "
{txt}
{com}. 
. local Z1  = " "
{txt}
{com}. local Z2  = " `medvars' `fedvars' "
{txt}
{com}. local Z3  = " age `medvars' m_age `fedvars' f_age "
{txt}
{com}. local Z4  = " age `medvars' m_age `fedvars' f_age `famvars' "
{txt}
{com}. local Z5  = " age med_scoll med_collplus m_age fed_scoll fed_collplus f_age `famvars' "
{txt}
{com}. local Z6  = " age med_scoll med_collplus m_age fed_scoll fed_collplus f_age `famvars' m_pc97 "
{txt}
{com}. 
. 
. local Zm0 = " age ln_wageFE_m `famvars' "
{txt}
{com}. local Zf0 = " age ln_wageFE_f `famvars' "
{txt}
{com}. local Z0  = " age ln_wageFE_m ln_wageFE_f `famvars'"
{txt}
{com}. 
. local Zm7 = " age ln_wageFE_m `famvars' "
{txt}
{com}. local Zf7 = " age ln_wageFE_f `famvars' "
{txt}
{com}. local Z7  = " age ln_wageFE_m ln_wageFE_f `famvars'"
{txt}
{com}. 
. 
. 
. 
. local Zall2  = " curr_married `medvars' `fedvars' "
{txt}
{com}. local Zall3  = " curr_married age `medvars' m_age fed_all_hs fed_all_scoll fed_all_collplus f_all_age "
{txt}
{com}. local Zall4  = " curr_married age `medvars' m_age fed_all_hs fed_all_scoll fed_all_collplus f_all_age `famvars' "
{txt}
{com}. local Zall5  = " curr_married age med_scoll med_collplus m_age fed_all_scoll fed_all_collplus f_all_age `famvars' "
{txt}
{com}. local Zall6  = " curr_married age med_scoll med_collplus m_age fed_all_scoll fed_all_collplus f_all_age `famvars' m_pc97 "
{txt}
{com}. 
. 
. gen m_pw_inst = m_pred_lnwage_mean_occ_state // Set main predicted wage instrumental variables for mothers
{txt}(476 missing values generated)

{com}. gen f_pw_inst = f_pred_lnwage_mean_occ_state // Set main predicted wage instrumental variables for fathers
{txt}(1,018 missing values generated)

{com}. 
. * Select staff ratio variable to include in specs
. *gen SR = staff_ratio     
. gen SR = staff_ratio_6m
{txt}(42 missing values generated)

{com}.         
. 
. * Generate an alternative father's education variable equal to zero (instead of missing) if unmarried
. gen fed_all_hs = fed_hs
{txt}(941 missing values generated)

{com}. gen fed_all_scoll = fed_scollplus
{txt}(941 missing values generated)

{com}. gen fed_all_collplus = fed_collplus
{txt}(941 missing values generated)

{com}. gen f_all_age = f_age
{txt}(966 missing values generated)

{com}. 
. replace fed_all_hs = 0 if curr_married==0
{txt}(873 real changes made)

{com}. replace fed_all_scoll = 0 if curr_married==0
{txt}(873 real changes made)

{com}. replace fed_all_collplus = 0 if curr_married==0
{txt}(873 real changes made)

{com}. replace f_all_age = 0 if curr_married==0
{txt}(873 real changes made)

{com}. 
. gen m_age_sq = m_age*m_age
{txt}
{com}. gen f_age_sq = f_age*f_age
{txt}(966 missing values generated)

{com}. 
. 
. 
. **************************************************************************************
. ********* D. PRELIMINARY ANALYSIS AND SUMMARY STATISTICS  ****************************
. **************************************************************************************
. 
. 
. *************************************   PREDICTED WORK PROBABILITIES  ******************************
. * Probabilities of positive hours observation (i.e. predicted probability of 'working') -- separately for married & single mothers
. 
. *Single mothers
. probit pos_hrs_m  `medvars' m_age `famvars' age_youngest i.age i.year   if curr_married==0

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-377.10538}  
Iteration 1:{space 3}log likelihood = {res:-353.99017}  
Iteration 2:{space 3}log likelihood = {res:-353.62041}  
Iteration 3:{space 3}log likelihood = {res:-353.61949}  
Iteration 4:{space 3}log likelihood = {res:-353.61949}  
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}       824
{txt}{col 49}LR chi2({res}17{txt}){col 67}= {res}     46.97
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0001
{txt}Log likelihood = {res}-353.61949{txt}{col 49}Pseudo R2{col 67}= {res}    0.0623

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   pos_hrs_m{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}med_hs {c |}{col 14}{res}{space 2} .4810121{col 26}{space 2} .1598858{col 37}{space 1}    3.01{col 46}{space 3}0.003{col 54}{space 4} .1676417{col 67}{space 3} .7943824
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2} .6431424{col 26}{space 2} .1658248{col 37}{space 1}    3.88{col 46}{space 3}0.000{col 54}{space 4} .3181317{col 67}{space 3} .9681531
{txt}med_collplus {c |}{col 14}{res}{space 2} 1.137953{col 26}{space 2} .2257454{col 37}{space 1}    5.04{col 46}{space 3}0.000{col 54}{space 4} .6955005{col 67}{space 3} 1.580406
{txt}{space 7}m_age {c |}{col 14}{res}{space 2}-.0224118{col 26}{space 2} .0093137{col 37}{space 1}   -2.41{col 46}{space 3}0.016{col 54}{space 4}-.0406663{col 67}{space 3}-.0041572
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .1989847{col 26}{space 2} .1223718{col 37}{space 1}    1.63{col 46}{space 3}0.104{col 54}{space 4}-.0408595{col 67}{space 3}  .438829
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2} -.100729{col 26}{space 2} .2294964{col 37}{space 1}   -0.44{col 46}{space 3}0.661{col 54}{space 4}-.5505337{col 67}{space 3} .3490758
{txt}{space 3}num_child {c |}{col 14}{res}{space 2}-.0158483{col 26}{space 2} .0707147{col 37}{space 1}   -0.22{col 46}{space 3}0.823{col 54}{space 4}-.1544466{col 67}{space 3} .1227499
{txt}age_youngest {c |}{col 14}{res}{space 2} .0309785{col 26}{space 2} .0390976{col 37}{space 1}    0.79{col 46}{space 3}0.428{col 54}{space 4}-.0456514{col 67}{space 3} .1076085
{txt}{space 12} {c |}
{space 9}age {c |}
{space 10}6  {c |}{col 14}{res}{space 2}-.1279133{col 26}{space 2} .3054715{col 37}{space 1}   -0.42{col 46}{space 3}0.675{col 54}{space 4}-.7266264{col 67}{space 3} .4707999
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0491985{col 26}{space 2} .2917387{col 37}{space 1}   -0.17{col 46}{space 3}0.866{col 54}{space 4}-.6209958{col 67}{space 3} .5225988
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.2109805{col 26}{space 2} .2943568{col 37}{space 1}   -0.72{col 46}{space 3}0.474{col 54}{space 4}-.7879092{col 67}{space 3} .3659482
{txt}{space 10}9  {c |}{col 14}{res}{space 2}  .010869{col 26}{space 2} .2921677{col 37}{space 1}    0.04{col 46}{space 3}0.970{col 54}{space 4}-.5617692{col 67}{space 3} .5835072
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0938311{col 26}{space 2} .2943638{col 37}{space 1}    0.32{col 46}{space 3}0.750{col 54}{space 4}-.4831114{col 67}{space 3} .6707737
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1735899{col 26}{space 2} .2825745{col 37}{space 1}    0.61{col 46}{space 3}0.539{col 54}{space 4} -.380246{col 67}{space 3} .7274257
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.1126532{col 26}{space 2} .2858281{col 37}{space 1}   -0.39{col 46}{space 3}0.693{col 54}{space 4} -.672866{col 67}{space 3} .4475595
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2002  {c |}{col 14}{res}{space 2} .0746243{col 26}{space 2} .1177008{col 37}{space 1}    0.63{col 46}{space 3}0.526{col 54}{space 4} -.156065{col 67}{space 3} .3053136
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} .0204517{col 26}{space 2} .1940249{col 37}{space 1}    0.11{col 46}{space 3}0.916{col 54}{space 4}  -.35983{col 67}{space 3} .4007334
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .9692509{col 26}{space 2} .5690099{col 37}{space 1}    1.70{col 46}{space 3}0.088{col 54}{space 4} -.145988{col 67}{space 3}  2.08449
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict prob_work_m0 if e(sample)
{txt}(option {bf:pr} assumed; Pr(pos_hrs_m))
(1,938 missing values generated)

{com}. eststo margin: margins, dydx(*) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}       824
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(pos_hrs_m), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:med_hs med_scoll med_collplus m_age m_white num_0_5 num_child age_youngest 6.age 7.age 8.age 9.age 10.age 11.age 12.age 2002.year 2007.year}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}med_hs {c |}{col 14}{res}{space 2} .1146787{col 26}{space 2} .0376566{col 37}{space 1}    3.05{col 46}{space 3}0.002{col 54}{space 4} .0408731{col 67}{space 3} .1884844
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2} .1533325{col 26}{space 2} .0387734{col 37}{space 1}    3.95{col 46}{space 3}0.000{col 54}{space 4}  .077338{col 67}{space 3}  .229327
{txt}med_collplus {c |}{col 14}{res}{space 2}  .271301{col 26}{space 2} .0525185{col 37}{space 1}    5.17{col 46}{space 3}0.000{col 54}{space 4} .1683666{col 67}{space 3} .3742355
{txt}{space 7}m_age {c |}{col 14}{res}{space 2}-.0053432{col 26}{space 2}  .002211{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-.0096767{col 67}{space 3}-.0010098
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .0474402{col 26}{space 2} .0290889{col 37}{space 1}    1.63{col 46}{space 3}0.103{col 54}{space 4} -.009573{col 67}{space 3} .1044535
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2}-.0240149{col 26}{space 2} .0547071{col 37}{space 1}   -0.44{col 46}{space 3}0.661{col 54}{space 4}-.1312388{col 67}{space 3} .0832089
{txt}{space 3}num_child {c |}{col 14}{res}{space 2}-.0037784{col 26}{space 2} .0168578{col 37}{space 1}   -0.22{col 46}{space 3}0.823{col 54}{space 4} -.036819{col 67}{space 3} .0292622
{txt}age_youngest {c |}{col 14}{res}{space 2} .0073856{col 26}{space 2} .0093141{col 37}{space 1}    0.79{col 46}{space 3}0.428{col 54}{space 4}-.0108697{col 67}{space 3}  .025641
{txt}{space 12} {c |}
{space 9}age {c |}
{space 10}6  {c |}{col 14}{res}{space 2}-.0318028{col 26}{space 2} .0750832{col 37}{space 1}   -0.42{col 46}{space 3}0.672{col 54}{space 4}-.1789631{col 67}{space 3} .1153575
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0118177{col 26}{space 2} .0696122{col 37}{space 1}   -0.17{col 46}{space 3}0.865{col 54}{space 4}-.1482552{col 67}{space 3} .1246197
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0543067{col 26}{space 2} .0737038{col 37}{space 1}   -0.74{col 46}{space 3}0.461{col 54}{space 4}-.1987635{col 67}{space 3} .0901501
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0025405{col 26}{space 2} .0683933{col 37}{space 1}    0.04{col 46}{space 3}0.970{col 54}{space 4} -.131508{col 67}{space 3}  .136589
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0210929{col 26}{space 2} .0672188{col 37}{space 1}    0.31{col 46}{space 3}0.754{col 54}{space 4}-.1106535{col 67}{space 3} .1528393
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .0375356{col 26}{space 2} .0635795{col 37}{space 1}    0.59{col 46}{space 3}0.555{col 54}{space 4}-.0870779{col 67}{space 3} .1621491
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.0278255{col 26}{space 2} .0692882{col 37}{space 1}   -0.40{col 46}{space 3}0.688{col 54}{space 4}-.1636278{col 67}{space 3} .1079768
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2002  {c |}{col 14}{res}{space 2} .0177776{col 26}{space 2} .0280802{col 37}{space 1}    0.63{col 46}{space 3}0.527{col 54}{space 4}-.0372586{col 67}{space 3} .0728138
{txt}{space 7}2007  {c |}{col 14}{res}{space 2}   .00499{col 26}{space 2} .0471048{col 37}{space 1}    0.11{col 46}{space 3}0.916{col 54}{space 4}-.0873337{col 67}{space 3} .0973137
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. estimates store est_work_m0
{txt}
{com}. 
. *Married mothers
. probit pos_hrs_m  `medvars' m_age `famvars' age_youngest `fedvars' f_age  i.age i.year  if curr_married==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-927.87848}  
Iteration 1:{space 3}log likelihood = {res:-879.91965}  
Iteration 2:{space 3}log likelihood = {res:-879.65877}  
Iteration 3:{space 3}log likelihood = {res:-879.65873}  
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     1,753
{txt}{col 49}LR chi2({res}21{txt}){col 67}= {res}     96.44
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-879.65873{txt}{col 49}Pseudo R2{col 67}= {res}    0.0520

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   pos_hrs_m{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}med_hs {c |}{col 14}{res}{space 2} .2662355{col 26}{space 2} .1596742{col 37}{space 1}    1.67{col 46}{space 3}0.095{col 54}{space 4}-.0467201{col 67}{space 3} .5791911
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2} .3935826{col 26}{space 2} .1683273{col 37}{space 1}    2.34{col 46}{space 3}0.019{col 54}{space 4} .0636672{col 67}{space 3} .7234981
{txt}med_collplus {c |}{col 14}{res}{space 2}  .670546{col 26}{space 2} .1775779{col 37}{space 1}    3.78{col 46}{space 3}0.000{col 54}{space 4} .3224996{col 67}{space 3} 1.018592
{txt}{space 7}m_age {c |}{col 14}{res}{space 2}-.0004711{col 26}{space 2} .0103101{col 37}{space 1}   -0.05{col 46}{space 3}0.964{col 54}{space 4}-.0206786{col 67}{space 3} .0197364
{txt}{space 5}m_white {c |}{col 14}{res}{space 2}-.0672082{col 26}{space 2} .0791245{col 37}{space 1}   -0.85{col 46}{space 3}0.396{col 54}{space 4}-.2222894{col 67}{space 3}  .087873
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2}-.1222538{col 26}{space 2} .1359681{col 37}{space 1}   -0.90{col 46}{space 3}0.369{col 54}{space 4}-.3887463{col 67}{space 3} .1442388
{txt}{space 3}num_child {c |}{col 14}{res}{space 2}-.0035601{col 26}{space 2} .0512027{col 37}{space 1}   -0.07{col 46}{space 3}0.945{col 54}{space 4}-.1039156{col 67}{space 3} .0967953
{txt}age_youngest {c |}{col 14}{res}{space 2} .0411091{col 26}{space 2} .0246733{col 37}{space 1}    1.67{col 46}{space 3}0.096{col 54}{space 4}-.0072497{col 67}{space 3}  .089468
{txt}{space 6}fed_hs {c |}{col 14}{res}{space 2} .4354252{col 26}{space 2} .1303031{col 37}{space 1}    3.34{col 46}{space 3}0.001{col 54}{space 4} .1800358{col 67}{space 3} .6908146
{txt}{space 3}fed_scoll {c |}{col 14}{res}{space 2}  .392083{col 26}{space 2} .1447177{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1084416{col 67}{space 3} .6757244
{txt}fed_collplus {c |}{col 14}{res}{space 2} .0804215{col 26}{space 2} .1469747{col 37}{space 1}    0.55{col 46}{space 3}0.584{col 54}{space 4}-.2076437{col 67}{space 3} .3684867
{txt}{space 7}f_age {c |}{col 14}{res}{space 2}-.0062676{col 26}{space 2}  .008389{col 37}{space 1}   -0.75{col 46}{space 3}0.455{col 54}{space 4}-.0227098{col 67}{space 3} .0101745
{txt}{space 12} {c |}
{space 9}age {c |}
{space 10}6  {c |}{col 14}{res}{space 2}-.2586561{col 26}{space 2} .1923849{col 37}{space 1}   -1.34{col 46}{space 3}0.179{col 54}{space 4}-.6357235{col 67}{space 3} .1184113
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0375476{col 26}{space 2} .1921697{col 37}{space 1}   -0.20{col 46}{space 3}0.845{col 54}{space 4}-.4141934{col 67}{space 3} .3390981
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0445138{col 26}{space 2} .1959184{col 37}{space 1}    0.23{col 46}{space 3}0.820{col 54}{space 4}-.3394791{col 67}{space 3} .4285068
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.1409483{col 26}{space 2} .1915734{col 37}{space 1}   -0.74{col 46}{space 3}0.462{col 54}{space 4}-.5164253{col 67}{space 3} .2345286
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.1413545{col 26}{space 2} .1921233{col 37}{space 1}   -0.74{col 46}{space 3}0.462{col 54}{space 4}-.5179093{col 67}{space 3} .2352004
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.1180882{col 26}{space 2} .1916762{col 37}{space 1}   -0.62{col 46}{space 3}0.538{col 54}{space 4}-.4937667{col 67}{space 3} .2575902
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.0115164{col 26}{space 2} .1961259{col 37}{space 1}   -0.06{col 46}{space 3}0.953{col 54}{space 4}-.3959161{col 67}{space 3} .3728832
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2002  {c |}{col 14}{res}{space 2} .2682043{col 26}{space 2} .0759274{col 37}{space 1}    3.53{col 46}{space 3}0.000{col 54}{space 4} .1193892{col 67}{space 3} .4170193
{txt}{space 7}2007  {c |}{col 14}{res}{space 2}-.0530953{col 26}{space 2} .1281191{col 37}{space 1}   -0.41{col 46}{space 3}0.679{col 54}{space 4}-.3042042{col 67}{space 3} .1980136
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .1381739{col 26}{space 2} .4063163{col 37}{space 1}    0.34{col 46}{space 3}0.734{col 54}{space 4}-.6581915{col 67}{space 3} .9345392
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict prob_work_m1 if e(sample)
{txt}(option {bf:pr} assumed; Pr(pos_hrs_m))
(1,009 missing values generated)

{com}. eststo margin: margins, dydx(*) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     1,753
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(pos_hrs_m), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:med_hs med_scoll med_collplus m_age m_white num_0_5 num_child age_youngest fed_hs fed_scoll fed_collplus f_age 6.age 7.age 8.age 9.age 10.age 11.age 12.age 2002.year 2007.year}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}med_hs {c |}{col 14}{res}{space 2}  .074934{col 26}{space 2} .0448387{col 37}{space 1}    1.67{col 46}{space 3}0.095{col 54}{space 4}-.0129483{col 67}{space 3} .1628163
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2} .1107768{col 26}{space 2} .0471724{col 37}{space 1}    2.35{col 46}{space 3}0.019{col 54}{space 4} .0183206{col 67}{space 3}  .203233
{txt}med_collplus {c |}{col 14}{res}{space 2} .1887302{col 26}{space 2} .0494658{col 37}{space 1}    3.82{col 46}{space 3}0.000{col 54}{space 4} .0917791{col 67}{space 3} .2856813
{txt}{space 7}m_age {c |}{col 14}{res}{space 2}-.0001326{col 26}{space 2} .0029019{col 37}{space 1}   -0.05{col 46}{space 3}0.964{col 54}{space 4}-.0058201{col 67}{space 3} .0055549
{txt}{space 5}m_white {c |}{col 14}{res}{space 2}-.0189163{col 26}{space 2} .0222611{col 37}{space 1}   -0.85{col 46}{space 3}0.395{col 54}{space 4}-.0625471{col 67}{space 3} .0247146
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2}-.0344092{col 26}{space 2} .0382493{col 37}{space 1}   -0.90{col 46}{space 3}0.368{col 54}{space 4}-.1093766{col 67}{space 3} .0405581
{txt}{space 3}num_child {c |}{col 14}{res}{space 2} -.001002{col 26}{space 2} .0144114{col 37}{space 1}   -0.07{col 46}{space 3}0.945{col 54}{space 4}-.0292478{col 67}{space 3} .0272437
{txt}age_youngest {c |}{col 14}{res}{space 2} .0115705{col 26}{space 2} .0069325{col 37}{space 1}    1.67{col 46}{space 3}0.095{col 54}{space 4}-.0020169{col 67}{space 3} .0251579
{txt}{space 6}fed_hs {c |}{col 14}{res}{space 2} .1225537{col 26}{space 2} .0363745{col 37}{space 1}    3.37{col 46}{space 3}0.001{col 54}{space 4}  .051261{col 67}{space 3} .1938464
{txt}{space 3}fed_scoll {c |}{col 14}{res}{space 2} .1103547{col 26}{space 2} .0405153{col 37}{space 1}    2.72{col 46}{space 3}0.006{col 54}{space 4} .0309462{col 67}{space 3} .1897632
{txt}fed_collplus {c |}{col 14}{res}{space 2} .0226352{col 26}{space 2} .0413561{col 37}{space 1}    0.55{col 46}{space 3}0.584{col 54}{space 4}-.0584211{col 67}{space 3} .1036916
{txt}{space 7}f_age {c |}{col 14}{res}{space 2}-.0017641{col 26}{space 2} .0023603{col 37}{space 1}   -0.75{col 46}{space 3}0.455{col 54}{space 4}-.0063902{col 67}{space 3}  .002862
{txt}{space 12} {c |}
{space 9}age {c |}
{space 10}6  {c |}{col 14}{res}{space 2}-.0750686{col 26}{space 2}   .05408{col 37}{space 1}   -1.39{col 46}{space 3}0.165{col 54}{space 4}-.1810635{col 67}{space 3} .0309263
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0100483{col 26}{space 2} .0511535{col 37}{space 1}   -0.20{col 46}{space 3}0.844{col 54}{space 4}-.1103073{col 67}{space 3} .0902108
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0115222{col 26}{space 2} .0510466{col 37}{space 1}    0.23{col 46}{space 3}0.821{col 54}{space 4}-.0885274{col 67}{space 3} .1115717
{txt}{space 10}9  {c |}{col 14}{res}{space 2} -.039242{col 26}{space 2} .0521597{col 37}{space 1}   -0.75{col 46}{space 3}0.452{col 54}{space 4}-.1414731{col 67}{space 3} .0629891
{txt}{space 9}10  {c |}{col 14}{res}{space 2} -.039361{col 26}{space 2} .0522356{col 37}{space 1}   -0.75{col 46}{space 3}0.451{col 54}{space 4}-.1417409{col 67}{space 3}  .063019
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.0325989{col 26}{space 2} .0517603{col 37}{space 1}   -0.63{col 46}{space 3}0.529{col 54}{space 4}-.1340472{col 67}{space 3} .0688494
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.0030501{col 26}{space 2} .0518455{col 37}{space 1}   -0.06{col 46}{space 3}0.953{col 54}{space 4}-.1046654{col 67}{space 3} .0985652
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2002  {c |}{col 14}{res}{space 2} .0740269{col 26}{space 2} .0207471{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .0333635{col 67}{space 3} .1146904
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} -.016394{col 26}{space 2} .0399574{col 37}{space 1}   -0.41{col 46}{space 3}0.682{col 54}{space 4}-.0947091{col 67}{space 3} .0619211
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. estimates store est_work_m1
{txt}
{com}. 
. *All mothers
. gen prob_work_m = prob_work_m0
{txt}(1,938 missing values generated)

{com}. replace prob_work_m = prob_work_m1 if curr_married==1
{txt}(1,753 real changes made)

{com}. 
. *Fathers
. probit pos_hrs_f  `medvars' m_age `famvars' age_youngest `fedvars' f_age  i.age i.year  if curr_married==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-423.32924}  
Iteration 1:{space 3}log likelihood = {res: -365.2469}  
Iteration 2:{space 3}log likelihood = {res:-360.47964}  
Iteration 3:{space 3}log likelihood = {res:-360.43988}  
Iteration 4:{space 3}log likelihood = {res:-360.43987}  
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     1,737
{txt}{col 49}LR chi2({res}21{txt}){col 67}= {res}    125.78
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-360.43987{txt}{col 49}Pseudo R2{col 67}= {res}    0.1486

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   pos_hrs_f{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}med_hs {c |}{col 14}{res}{space 2}-.2190238{col 26}{space 2} .2327932{col 37}{space 1}   -0.94{col 46}{space 3}0.347{col 54}{space 4}-.6752901{col 67}{space 3} .2372425
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2}-.0711218{col 26}{space 2} .2470384{col 37}{space 1}   -0.29{col 46}{space 3}0.773{col 54}{space 4}-.5553082{col 67}{space 3} .4130646
{txt}med_collplus {c |}{col 14}{res}{space 2} .2517871{col 26}{space 2}  .274721{col 37}{space 1}    0.92{col 46}{space 3}0.359{col 54}{space 4}-.2866561{col 67}{space 3} .7902304
{txt}{space 7}m_age {c |}{col 14}{res}{space 2} .0121462{col 26}{space 2} .0148429{col 37}{space 1}    0.82{col 46}{space 3}0.413{col 54}{space 4}-.0169453{col 67}{space 3} .0412376
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .5029358{col 26}{space 2} .1047564{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4}  .297617{col 67}{space 3} .7082545
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2} .0574705{col 26}{space 2} .2055824{col 37}{space 1}    0.28{col 46}{space 3}0.780{col 54}{space 4}-.3454635{col 67}{space 3} .4604046
{txt}{space 3}num_child {c |}{col 14}{res}{space 2}-.0916371{col 26}{space 2} .0669869{col 37}{space 1}   -1.37{col 46}{space 3}0.171{col 54}{space 4} -.222929{col 67}{space 3} .0396547
{txt}age_youngest {c |}{col 14}{res}{space 2} .0178876{col 26}{space 2} .0355964{col 37}{space 1}    0.50{col 46}{space 3}0.615{col 54}{space 4}-.0518799{col 67}{space 3} .0876552
{txt}{space 6}fed_hs {c |}{col 14}{res}{space 2} .0656371{col 26}{space 2} .1792922{col 37}{space 1}    0.37{col 46}{space 3}0.714{col 54}{space 4}-.2857692{col 67}{space 3} .4170434
{txt}{space 3}fed_scoll {c |}{col 14}{res}{space 2} .0436445{col 26}{space 2} .2017723{col 37}{space 1}    0.22{col 46}{space 3}0.829{col 54}{space 4}-.3518219{col 67}{space 3} .4391109
{txt}fed_collplus {c |}{col 14}{res}{space 2}   .64156{col 26}{space 2} .2345838{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4} .1817841{col 67}{space 3} 1.101336
{txt}{space 7}f_age {c |}{col 14}{res}{space 2}-.0190526{col 26}{space 2} .0116743{col 37}{space 1}   -1.63{col 46}{space 3}0.103{col 54}{space 4}-.0419338{col 67}{space 3} .0038285
{txt}{space 12} {c |}
{space 9}age {c |}
{space 10}6  {c |}{col 14}{res}{space 2}-.0598223{col 26}{space 2} .2825865{col 37}{space 1}   -0.21{col 46}{space 3}0.832{col 54}{space 4}-.6136815{col 67}{space 3}  .494037
{txt}{space 10}7  {c |}{col 14}{res}{space 2}  .146866{col 26}{space 2} .2949483{col 37}{space 1}    0.50{col 46}{space 3}0.619{col 54}{space 4}-.4312221{col 67}{space 3} .7249542
{txt}{space 10}8  {c |}{col 14}{res}{space 2}  .071932{col 26}{space 2}  .285316{col 37}{space 1}    0.25{col 46}{space 3}0.801{col 54}{space 4}-.4872771{col 67}{space 3} .6311412
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0535654{col 26}{space 2} .2803418{col 37}{space 1}    0.19{col 46}{space 3}0.848{col 54}{space 4}-.4958945{col 67}{space 3} .6030252
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.1367323{col 26}{space 2} .2724513{col 37}{space 1}   -0.50{col 46}{space 3}0.616{col 54}{space 4}-.6707271{col 67}{space 3} .3972625
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.0603061{col 26}{space 2} .2738665{col 37}{space 1}   -0.22{col 46}{space 3}0.826{col 54}{space 4}-.5970746{col 67}{space 3} .4764623
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.1159858{col 26}{space 2} .2779151{col 37}{space 1}   -0.42{col 46}{space 3}0.676{col 54}{space 4}-.6606895{col 67}{space 3} .4287179
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2002  {c |}{col 14}{res}{space 2} .6500221{col 26}{space 2} .1212753{col 37}{space 1}    5.36{col 46}{space 3}0.000{col 54}{space 4} .4123269{col 67}{space 3} .8877172
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} .7226543{col 26}{space 2} .2309794{col 37}{space 1}    3.13{col 46}{space 3}0.002{col 54}{space 4}  .269943{col 67}{space 3} 1.175366
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 1.199879{col 26}{space 2} .5861954{col 37}{space 1}    2.05{col 46}{space 3}0.041{col 54}{space 4} .0509567{col 67}{space 3}   2.3488
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict prob_work_f if e(sample)
{txt}(option {bf:pr} assumed; Pr(pos_hrs_f))
(1,025 missing values generated)

{com}. eststo margin: margins, dydx(*) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     1,737
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(pos_hrs_f), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:med_hs med_scoll med_collplus m_age m_white num_0_5 num_child age_youngest fed_hs fed_scoll fed_collplus f_age 6.age 7.age 8.age 9.age 10.age 11.age 12.age 2002.year 2007.year}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}med_hs {c |}{col 14}{res}{space 2}-.0243198{col 26}{space 2} .0258625{col 37}{space 1}   -0.94{col 46}{space 3}0.347{col 54}{space 4}-.0750093{col 67}{space 3} .0263697
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2}-.0078972{col 26}{space 2} .0274336{col 37}{space 1}   -0.29{col 46}{space 3}0.773{col 54}{space 4} -.061666{col 67}{space 3} .0458717
{txt}med_collplus {c |}{col 14}{res}{space 2} .0279578{col 26}{space 2} .0305234{col 37}{space 1}    0.92{col 46}{space 3}0.360{col 54}{space 4} -.031867{col 67}{space 3} .0877825
{txt}{space 7}m_age {c |}{col 14}{res}{space 2} .0013487{col 26}{space 2} .0016489{col 37}{space 1}    0.82{col 46}{space 3}0.413{col 54}{space 4}-.0018831{col 67}{space 3} .0045804
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .0558446{col 26}{space 2} .0117662{col 37}{space 1}    4.75{col 46}{space 3}0.000{col 54}{space 4} .0327834{col 67}{space 3} .0789059
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2} .0063814{col 26}{space 2} .0228315{col 37}{space 1}    0.28{col 46}{space 3}0.780{col 54}{space 4}-.0383676{col 67}{space 3} .0511303
{txt}{space 3}num_child {c |}{col 14}{res}{space 2}-.0101751{col 26}{space 2} .0074494{col 37}{space 1}   -1.37{col 46}{space 3}0.172{col 54}{space 4}-.0247757{col 67}{space 3} .0044255
{txt}age_youngest {c |}{col 14}{res}{space 2} .0019862{col 26}{space 2} .0039541{col 37}{space 1}    0.50{col 46}{space 3}0.615{col 54}{space 4}-.0057637{col 67}{space 3} .0097361
{txt}{space 6}fed_hs {c |}{col 14}{res}{space 2} .0072882{col 26}{space 2} .0199074{col 37}{space 1}    0.37{col 46}{space 3}0.714{col 54}{space 4}-.0317296{col 67}{space 3} .0463059
{txt}{space 3}fed_scoll {c |}{col 14}{res}{space 2} .0048462{col 26}{space 2} .0224073{col 37}{space 1}    0.22{col 46}{space 3}0.829{col 54}{space 4}-.0390713{col 67}{space 3} .0487636
{txt}fed_collplus {c |}{col 14}{res}{space 2} .0712371{col 26}{space 2} .0262335{col 37}{space 1}    2.72{col 46}{space 3}0.007{col 54}{space 4} .0198203{col 67}{space 3} .1226538
{txt}{space 7}f_age {c |}{col 14}{res}{space 2}-.0021156{col 26}{space 2} .0012976{col 37}{space 1}   -1.63{col 46}{space 3}0.103{col 54}{space 4}-.0046587{col 67}{space 3} .0004276
{txt}{space 12} {c |}
{space 9}age {c |}
{space 10}6  {c |}{col 14}{res}{space 2}-.0066887{col 26}{space 2} .0312668{col 37}{space 1}   -0.21{col 46}{space 3}0.831{col 54}{space 4}-.0679705{col 67}{space 3} .0545932
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0143386{col 26}{space 2} .0295661{col 37}{space 1}    0.48{col 46}{space 3}0.628{col 54}{space 4}-.0436099{col 67}{space 3}  .072287
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0073804{col 26}{space 2}  .029719{col 37}{space 1}    0.25{col 46}{space 3}0.804{col 54}{space 4}-.0508677{col 67}{space 3} .0656285
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0055628{col 26}{space 2} .0294758{col 37}{space 1}    0.19{col 46}{space 3}0.850{col 54}{space 4}-.0522088{col 67}{space 3} .0633343
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0160582{col 26}{space 2} .0309376{col 37}{space 1}   -0.52{col 46}{space 3}0.604{col 54}{space 4}-.0766947{col 67}{space 3} .0445783
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.0067449{col 26}{space 2}  .030145{col 37}{space 1}   -0.22{col 46}{space 3}0.823{col 54}{space 4}-.0658279{col 67}{space 3} .0523382
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.0134433{col 26}{space 2} .0314257{col 37}{space 1}   -0.43{col 46}{space 3}0.669{col 54}{space 4}-.0750366{col 67}{space 3} .0481499
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2002  {c |}{col 14}{res}{space 2} .0708372{col 26}{space 2} .0123118{col 37}{space 1}    5.75{col 46}{space 3}0.000{col 54}{space 4} .0467065{col 67}{space 3}  .094968
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} .0752607{col 26}{space 2} .0166155{col 37}{space 1}    4.53{col 46}{space 3}0.000{col 54}{space 4} .0426948{col 67}{space 3} .1078265
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. estimates store est_work_f
{txt}
{com}. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-2: Predicted probability (average derivatives) of work probits for parents
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab  est_work_m0 est_work_m1 est_work_f using "$tables/prob_work.tex",  coeflab(curr_married "Married"  age "Child's age" age_youngest "Age of youngest child" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age"  fed_hs "Father HS grad"  fed_scoll "Father some coll."  fed_collplus "Father coll+" f_age "Father's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children") stats(N, fmt(0))  ti(Appendix Table: Predicted probability (average derivatives) of work probits for parents (1-2 kids ages 0-12)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(30) modelwidth(10) wrap style(tex)  mtitles("Single Mothers" "Married Mothers"  "Married Fathers" )  replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/prob_work.tex"'})

{com}. 
. 
. 
. sort kid year
{txt}
{com}. save  "$temp/allfam_tmp.dta", replace                        // save data
{txt}file D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\code\estimation\basics_rel_demand\temp_data/allfam_tmp.dta saved

{com}. 
. 
. *************************************  LOG WAGE REGRESSIONS  ******************************
. * restricting to high work probabilities
. 
. 
. 
. regress ln_wage_m med_scoll med_collplus m_age m_age_sq m_white    if (curr_married==0 & prob_work_m >= `p_work_min_m' & prob_work_m !=.)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       542
{txt}{hline 13}{c +}{hline 34}   F(5, 536)       = {res}    18.70
{txt}       Model {c |} {res} 28.8326666         5  5.76653332   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 165.323842       536  .308440003   {txt}R-squared       ={res}    0.1485
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1406
{txt}       Total {c |} {res} 194.156508       541  .358884488   {txt}Root MSE        =   {res} .55537

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   ln_wage_m{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}med_scoll {c |}{col 14}{res}{space 2}  .245838{col 26}{space 2}  .053443{col 37}{space 1}    4.60{col 46}{space 3}0.000{col 54}{space 4} .1408545{col 67}{space 3} .3508215
{txt}med_collplus {c |}{col 14}{res}{space 2} .5255537{col 26}{space 2} .0665437{col 37}{space 1}    7.90{col 46}{space 3}0.000{col 54}{space 4} .3948353{col 67}{space 3} .6562721
{txt}{space 7}m_age {c |}{col 14}{res}{space 2} .0675438{col 26}{space 2} .0381572{col 37}{space 1}    1.77{col 46}{space 3}0.077{col 54}{space 4}-.0074122{col 67}{space 3} .1424998
{txt}{space 4}m_age_sq {c |}{col 14}{res}{space 2} -.000716{col 26}{space 2}  .000528{col 37}{space 1}   -1.36{col 46}{space 3}0.176{col 54}{space 4}-.0017533{col 67}{space 3} .0003212
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .1325332{col 26}{space 2}  .049945{col 37}{space 1}    2.65{col 46}{space 3}0.008{col 54}{space 4} .0344212{col 67}{space 3} .2306452
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6137976{col 26}{space 2} .6785322{col 37}{space 1}    0.90{col 46}{space 3}0.366{col 54}{space 4}-.7191108{col 67}{space 3} 1.946706
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store est_wage_m0
{txt}
{com}. 
. regress ln_wage_m med_scoll med_collplus m_age m_age_sq m_white    if (curr_married==1 & prob_work_m >= `p_work_min_m' & prob_work_m !=.)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       932
{txt}{hline 13}{c +}{hline 34}   F(5, 926)       = {res}    40.26
{txt}       Model {c |} {res} 66.4118569         5  13.2823714   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 305.530856       926  .329946928   {txt}R-squared       ={res}    0.1786
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1741
{txt}       Total {c |} {res} 371.942713       931  .399508821   {txt}Root MSE        =   {res} .57441

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   ln_wage_m{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}med_scoll {c |}{col 14}{res}{space 2} .2572491{col 26}{space 2} .0493834{col 37}{space 1}    5.21{col 46}{space 3}0.000{col 54}{space 4} .1603327{col 67}{space 3} .3541655
{txt}med_collplus {c |}{col 14}{res}{space 2} .5848996{col 26}{space 2} .0477578{col 37}{space 1}   12.25{col 46}{space 3}0.000{col 54}{space 4} .4911735{col 67}{space 3} .6786257
{txt}{space 7}m_age {c |}{col 14}{res}{space 2} .0554489{col 26}{space 2} .0327307{col 37}{space 1}    1.69{col 46}{space 3}0.091{col 54}{space 4}-.0087861{col 67}{space 3} .1196839
{txt}{space 4}m_age_sq {c |}{col 14}{res}{space 2}-.0005455{col 26}{space 2} .0004273{col 37}{space 1}   -1.28{col 46}{space 3}0.202{col 54}{space 4}-.0013841{col 67}{space 3}  .000293
{txt}{space 5}m_white {c |}{col 14}{res}{space 2}-.0444799{col 26}{space 2} .0424038{col 37}{space 1}   -1.05{col 46}{space 3}0.294{col 54}{space 4}-.1276986{col 67}{space 3} .0387387
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .963059{col 26}{space 2} .6201855{col 37}{space 1}    1.55{col 46}{space 3}0.121{col 54}{space 4}-.2540731{col 67}{space 3} 2.180191
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store est_wage_m1
{txt}
{com}. 
. regress ln_wage_f `fedvars' f_age f_age_sq m_white                 if (curr_married==1 & prob_work_f >= `p_work_min_f' & prob_work_f !=.)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,182
{txt}{hline 13}{c +}{hline 34}   F(6, 1175)      = {res}    56.15
{txt}       Model {c |} {res} 98.6154967         6  16.4359161   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 343.969498     1,175  .292739998   {txt}R-squared       ={res}    0.2228
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2188
{txt}       Total {c |} {res} 442.584995     1,181  .374754441   {txt}Root MSE        =   {res} .54105

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   ln_wage_f{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}fed_hs {c |}{col 14}{res}{space 2} .3074231{col 26}{space 2} .0664471{col 37}{space 1}    4.63{col 46}{space 3}0.000{col 54}{space 4} .1770548{col 67}{space 3} .4377913
{txt}{space 3}fed_scoll {c |}{col 14}{res}{space 2} .4764098{col 26}{space 2} .0691515{col 37}{space 1}    6.89{col 46}{space 3}0.000{col 54}{space 4} .3407356{col 67}{space 3} .6120841
{txt}fed_collplus {c |}{col 14}{res}{space 2} .7810485{col 26}{space 2}  .066102{col 37}{space 1}   11.82{col 46}{space 3}0.000{col 54}{space 4} .6513574{col 67}{space 3} .9107396
{txt}{space 7}f_age {c |}{col 14}{res}{space 2} .0717735{col 26}{space 2} .0195653{col 37}{space 1}    3.67{col 46}{space 3}0.000{col 54}{space 4} .0333867{col 67}{space 3} .1101602
{txt}{space 4}f_age_sq {c |}{col 14}{res}{space 2}-.0007954{col 26}{space 2} .0002362{col 37}{space 1}   -3.37{col 46}{space 3}0.001{col 54}{space 4}-.0012588{col 67}{space 3} -.000332
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .1782525{col 26}{space 2} .0408694{col 37}{space 1}    4.36{col 46}{space 3}0.000{col 54}{space 4} .0980674{col 67}{space 3} .2584376
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .769347{col 26}{space 2} .4014922{col 37}{space 1}    1.92{col 46}{space 3}0.056{col 54}{space 4}-.0183746{col 67}{space 3} 1.557069
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store est_wage_f
{txt}
{com}. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-3: Log wage regressions for parents
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab  est_wage_m0 est_wage_m1 est_wage_f using "$tables/wage_regs_pwork.tex", rename(f* m*) coeflab(curr_married "Married"  age "Child's age" med_hs "Parent HS grad"  med_scoll "Parent some coll."  med_collplus "Parent coll+"  m_age "Parent's age"  m_age_sq "Parent's age squared" fed_hs "Parent HS grad"  fed_scoll "Parent some coll."  fed_collplus "Parent coll+"  f_age "Parent's age"  f_age_sq "Parent's age squared" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children") stats(r2 N, fmt(3 0))  ti(Appendix Table: Log wage regressions for parents (1-2 kids ages 0-12, P(work) $ \geq $ `p_work_min_m' or `p_work_min_f')) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(30) modelwidth(15) wrap style(tex)  mtitles( "Single Mothers" "Married Mothers"  "Married Fathers")  replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/wage_regs_pwork.tex"'})

{com}. 
. 
. 
. 
. ************************* PREDICTED POSITIVE CHILDCARE EXPENDITURES ***************************************
. 
. * Look at whether positive childcare expenditures is related to family characteristics & prices
. 
. local pccare = "pos_chcare_imp"   
{txt}
{com}. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)

{com}. 
. 
. * no restrictions on probability of work
. 
. foreach pvar in 4ca{c -(}
{txt}  2{com}. 
.     disp  _n(1) "Price measure: `pvar'" _n(1)   
{txt}  3{com}. 
.         disp _n(2) "Probability of positive child care expenditures"
{txt}  4{com}.         probit `pccare'   ln_pratio_`pvar' `Zm5' curr_married i.year  ind_same_state old_child
{txt}  5{com}.                 eststo margin: margins, dydx(*) post
{txt}  6{com}.         estimates store est_all5
{txt}  7{com}.         
. 
.         
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-13: Probit estimates for positive child care expenditures
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab  est_all5  using "$tables/pos_childcare_`pvar'.tex", coeflab(curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age"  fed_hs "Father HS grad"  fed_scoll "Father some coll."  fed_collplus "Father coll+" f_age "Father's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children" ind_same_state "HH Head same state" old_child "Any children ages 13+" old_child_2plus "2+ children ages 13+") stats(N, fmt(0))  ti(Appendix Table: Probit estimates for positive child care expenditures (1-2 kids ages 0-12, `pccare', 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(30) modelwidth(10) wrap style(tex) mtitles("All Mothers" "All Mothers")   replace
{txt}  8{com}. 
. {c )-}

Price measure: 4ca



Probability of positive child care expenditures

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -827.1668}  
Iteration 1:{space 3}log likelihood = {res: -687.8406}  
Iteration 2:{space 3}log likelihood = {res:-684.72256}  
Iteration 3:{space 3}log likelihood = {res:-684.71106}  
Iteration 4:{space 3}log likelihood = {res:-684.71106}  
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     1,391
{txt}{col 49}LR chi2({res}12{txt}){col 67}= {res}    284.91
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-684.71106{txt}{col 49}Pseudo R2{col 67}= {res}    0.1722

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}pos_chcare_imp{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}ln_pratio_4ca {c |}{col 16}{res}{space 2} .1269732{col 28}{space 2} .1261286{col 39}{space 1}    1.01{col 48}{space 3}0.314{col 56}{space 4}-.1202342{col 69}{space 3} .3741807
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.1443985{col 28}{space 2}  .022229{col 39}{space 1}   -6.50{col 48}{space 3}0.000{col 56}{space 4}-.1879665{col 69}{space 3}-.1008306
{txt}{space 5}med_scoll {c |}{col 16}{res}{space 2} .3520593{col 28}{space 2}  .092998{col 39}{space 1}    3.79{col 48}{space 3}0.000{col 56}{space 4} .1697865{col 69}{space 3}  .534332
{txt}{space 2}med_collplus {c |}{col 16}{res}{space 2}  .431934{col 28}{space 2} .1019924{col 39}{space 1}    4.23{col 48}{space 3}0.000{col 56}{space 4} .2320326{col 69}{space 3} .6318354
{txt}{space 9}m_age {c |}{col 16}{res}{space 2}-.0058314{col 28}{space 2} .0072923{col 39}{space 1}   -0.80{col 48}{space 3}0.424{col 56}{space 4}-.0201241{col 69}{space 3} .0084613
{txt}{space 7}m_white {c |}{col 16}{res}{space 2}-.1029061{col 28}{space 2} .0888542{col 39}{space 1}   -1.16{col 48}{space 3}0.247{col 56}{space 4}-.2770572{col 69}{space 3} .0712449
{txt}{space 7}num_0_5 {c |}{col 16}{res}{space 2} .3221254{col 28}{space 2} .0994765{col 39}{space 1}    3.24{col 48}{space 3}0.001{col 56}{space 4} .1271551{col 69}{space 3} .5170957
{txt}{space 5}num_child {c |}{col 16}{res}{space 2}-.1936677{col 28}{space 2} .0788738{col 39}{space 1}   -2.46{col 48}{space 3}0.014{col 56}{space 4}-.3482576{col 69}{space 3}-.0390778
{txt}{space 2}curr_married {c |}{col 16}{res}{space 2}-.1019906{col 28}{space 2} .0925563{col 39}{space 1}   -1.10{col 48}{space 3}0.270{col 56}{space 4}-.2833977{col 69}{space 3} .0794164
{txt}{space 14} {c |}
{space 10}year {c |}
{space 9}2007  {c |}{col 16}{res}{space 2}-.7862582{col 28}{space 2} .1374761{col 39}{space 1}   -5.72{col 48}{space 3}0.000{col 56}{space 4}-1.055706{col 69}{space 3}  -.51681
{txt}{space 14} {c |}
ind_same_state {c |}{col 16}{res}{space 2}-.0557129{col 28}{space 2} .0876553{col 39}{space 1}   -0.64{col 48}{space 3}0.525{col 56}{space 4}-.2275141{col 69}{space 3} .1160884
{txt}{space 5}old_child {c |}{col 16}{res}{space 2}-.1635925{col 28}{space 2} .1193911{col 39}{space 1}   -1.37{col 48}{space 3}0.171{col 56}{space 4}-.3975947{col 69}{space 3} .0704097
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 1.236163{col 28}{space 2} .3666139{col 39}{space 1}    3.37{col 48}{space 3}0.001{col 56}{space 4} .5176134{col 69}{space 3} 1.954714
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     1,391
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(pos_chcare_imp), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:ln_pratio_4ca age med_scoll med_collplus m_age m_white num_0_5 num_child curr_married 2007.year ind_same_state old_child}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}ln_pratio_4ca {c |}{col 16}{res}{space 2} .0352451{col 28}{space 2} .0349858{col 39}{space 1}    1.01{col 48}{space 3}0.314{col 56}{space 4}-.0333258{col 69}{space 3} .1038161
{txt}{space 11}age {c |}{col 16}{res}{space 2} -.040082{col 28}{space 2} .0058699{col 39}{space 1}   -6.83{col 48}{space 3}0.000{col 56}{space 4}-.0515869{col 69}{space 3}-.0285772
{txt}{space 5}med_scoll {c |}{col 16}{res}{space 2} .0977243{col 28}{space 2} .0254487{col 39}{space 1}    3.84{col 48}{space 3}0.000{col 56}{space 4} .0478457{col 69}{space 3} .1476029
{txt}{space 2}med_collplus {c |}{col 16}{res}{space 2} .1198959{col 28}{space 2} .0278083{col 39}{space 1}    4.31{col 48}{space 3}0.000{col 56}{space 4} .0653927{col 69}{space 3} .1743991
{txt}{space 9}m_age {c |}{col 16}{res}{space 2}-.0016187{col 28}{space 2} .0020223{col 39}{space 1}   -0.80{col 48}{space 3}0.423{col 56}{space 4}-.0055824{col 69}{space 3}  .002345
{txt}{space 7}m_white {c |}{col 16}{res}{space 2}-.0285646{col 28}{space 2} .0246305{col 39}{space 1}   -1.16{col 48}{space 3}0.246{col 56}{space 4}-.0768396{col 69}{space 3} .0197104
{txt}{space 7}num_0_5 {c |}{col 16}{res}{space 2} .0894153{col 28}{space 2} .0273206{col 39}{space 1}    3.27{col 48}{space 3}0.001{col 56}{space 4}  .035868{col 69}{space 3} .1429626
{txt}{space 5}num_child {c |}{col 16}{res}{space 2}-.0537581{col 28}{space 2}   .02177{col 39}{space 1}   -2.47{col 48}{space 3}0.014{col 56}{space 4}-.0964265{col 69}{space 3}-.0110897
{txt}{space 2}curr_married {c |}{col 16}{res}{space 2}-.0283105{col 28}{space 2} .0256583{col 39}{space 1}   -1.10{col 48}{space 3}0.270{col 56}{space 4}-.0785998{col 69}{space 3} .0219788
{txt}{space 14} {c |}
{space 10}year {c |}
{space 9}2007  {c |}{col 16}{res}{space 2}-.1944195{col 28}{space 2} .0278652{col 39}{space 1}   -6.98{col 48}{space 3}0.000{col 56}{space 4}-.2490342{col 69}{space 3}-.1398048
{txt}{space 14} {c |}
ind_same_state {c |}{col 16}{res}{space 2}-.0154647{col 28}{space 2} .0243187{col 39}{space 1}   -0.64{col 48}{space 3}0.525{col 56}{space 4}-.0631285{col 69}{space 3}  .032199
{txt}{space 5}old_child {c |}{col 16}{res}{space 2}-.0454099{col 28}{space 2} .0330856{col 39}{space 1}   -1.37{col 48}{space 3}0.170{col 56}{space 4}-.1102565{col 69}{space 3} .0194367
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/pos_childcare_4ca.tex"'})

{com}. 
. 
. ************************* SUMMARY STATS 2002 & 2007 FOR FULL VS. RESTRICTED SAMPLES ***************************************
. 
. 
. generate year2007=(year==2007)
{txt}
{com}. replace year2007=. if year==.
{txt}(0 real changes made)

{com}. 
. 
. 
. disp "Summary statistics for full sample: 2002 and 2007"
{res}Summary statistics for full sample: 2002 and 2007
{txt}
{com}. eststo t_all: estpost summ ln_wage_m_rel ln_wage_f_rel ln_pratio_4ca age `medvars' m_age `fedvars' f_age `famvars' year2007

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:ln_wage_m_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1110}}}{space 1}{space 1}{ralign 9:{res:{sf:     1110}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.437403}}}{space 1}{space 1}{ralign 9:{res:{sf: .4378415}}}{space 1}{space 1}{ralign 9:{res:{sf: .6616959}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.074251}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.991448}}}{space 1}{space 1}{ralign 9:{res:{sf: 2705.518}}}{space 1}
{space 0}{space 0}{ralign 12:ln_wage_f_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      835}}}{space 1}{space 1}{ralign 9:{res:{sf:      835}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.926646}}}{space 1}{space 1}{ralign 9:{res:{sf: .3594853}}}{space 1}{space 1}{ralign 9:{res:{sf: .5995709}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.251684}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.904192}}}{space 1}{space 1}{ralign 9:{res:{sf: 2443.749}}}{space 1}
{space 0}{space 0}{ralign 12:ln_pratio_~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf:  1.10495}}}{space 1}{space 1}{ralign 9:{res:{sf: .1005264}}}{space 1}{space 1}{ralign 9:{res:{sf:  .317059}}}{space 1}{space 1}{ralign 9:{res:{sf: .2739018}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.891691}}}{space 1}{space 1}{ralign 9:{res:{sf: 1670.684}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.527116}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.408265}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.099587}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:       12}}}{space 1}{space 1}{ralign 9:{res:{sf:    14405}}}{space 1}
{space 0}{space 0}{ralign 12:med_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1510}}}{space 1}{space 1}{ralign 9:{res:{sf:     1510}}}{space 1}{space 1}{ralign 9:{res:{sf: .3278146}}}{space 1}{space 1}{ralign 9:{res:{sf: .2204982}}}{space 1}{space 1}{ralign 9:{res:{sf: .4695724}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      495}}}{space 1}
{space 0}{space 0}{ralign 12:med_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1510}}}{space 1}{space 1}{ralign 9:{res:{sf:     1510}}}{space 1}{space 1}{ralign 9:{res:{sf: .3218543}}}{space 1}{space 1}{ralign 9:{res:{sf: .2184088}}}{space 1}{space 1}{ralign 9:{res:{sf: .4673422}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      486}}}{space 1}
{space 0}{space 0}{ralign 12:med_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1510}}}{space 1}{space 1}{ralign 9:{res:{sf:     1510}}}{space 1}{space 1}{ralign 9:{res:{sf: .2688742}}}{space 1}{space 1}{ralign 9:{res:{sf: .1967111}}}{space 1}{space 1}{ralign 9:{res:{sf: .4435213}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      406}}}{space 1}
{space 0}{space 0}{ralign 12:m_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf: 37.56085}}}{space 1}{space 1}{ralign 9:{res:{sf: 41.39802}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.434129}}}{space 1}{space 1}{ralign 9:{res:{sf:       21}}}{space 1}{space 1}{ralign 9:{res:{sf:       55}}}{space 1}{space 1}{ralign 9:{res:{sf:    56792}}}{space 1}
{space 0}{space 0}{ralign 12:fed_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      951}}}{space 1}{space 1}{ralign 9:{res:{sf:      951}}}{space 1}{space 1}{ralign 9:{res:{sf: .3575184}}}{space 1}{space 1}{ralign 9:{res:{sf: .2299408}}}{space 1}{space 1}{ralign 9:{res:{sf: .4795214}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      340}}}{space 1}
{space 0}{space 0}{ralign 12:fed_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      951}}}{space 1}{space 1}{ralign 9:{res:{sf:      951}}}{space 1}{space 1}{ralign 9:{res:{sf: .2239748}}}{space 1}{space 1}{ralign 9:{res:{sf:  .173993}}}{space 1}{space 1}{ralign 9:{res:{sf: .4171247}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      213}}}{space 1}
{space 0}{space 0}{ralign 12:fed_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      951}}}{space 1}{space 1}{ralign 9:{res:{sf:      951}}}{space 1}{space 1}{ralign 9:{res:{sf: .3259727}}}{space 1}{space 1}{ralign 9:{res:{sf: .2199458}}}{space 1}{space 1}{ralign 9:{res:{sf: .4689838}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      310}}}{space 1}
{space 0}{space 0}{ralign 12:f_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      937}}}{space 1}{space 1}{ralign 9:{res:{sf:      937}}}{space 1}{space 1}{ralign 9:{res:{sf: 40.49626}}}{space 1}{space 1}{ralign 9:{res:{sf: 49.62632}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.044595}}}{space 1}{space 1}{ralign 9:{res:{sf:       20}}}{space 1}{space 1}{ralign 9:{res:{sf:       65}}}{space 1}{space 1}{ralign 9:{res:{sf:    37945}}}{space 1}
{space 0}{space 0}{ralign 12:m_white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1499}}}{space 1}{space 1}{ralign 9:{res:{sf:     1499}}}{space 1}{space 1}{ralign 9:{res:{sf: .5763843}}}{space 1}{space 1}{ralign 9:{res:{sf: .2443284}}}{space 1}{space 1}{ralign 9:{res:{sf: .4942959}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      864}}}{space 1}
{space 0}{space 0}{ralign 12:num_0_5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf: .1917989}}}{space 1}{space 1}{ralign 9:{res:{sf: .1736455}}}{space 1}{space 1}{ralign 9:{res:{sf: .4167079}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:      290}}}{space 1}
{space 0}{space 0}{ralign 12:num_child}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.022487}}}{space 1}{space 1}{ralign 9:{res:{sf:  .531592}}}{space 1}{space 1}{ralign 9:{res:{sf: .7291035}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        6}}}{space 1}{space 1}{ralign 9:{res:{sf:     3058}}}{space 1}
{space 0}{space 0}{ralign 12:year2007}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf:     1512}}}{space 1}{space 1}{ralign 9:{res:{sf: .2208995}}}{space 1}{space 1}{ralign 9:{res:{sf: .1722168}}}{space 1}{space 1}{ralign 9:{res:{sf: .4149901}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      334}}}{space 1}

{com}. 
. disp "Summary statistics for sample with high predicted probability of work for mothers: 2002 and 2007" 
{res}Summary statistics for sample with high predicted probability of work for mothers: 2002 and 2007
{txt}
{com}. eststo t_rest_prob: estpost summ ln_wage_m_rel ln_wage_f_rel ln_pratio_4ca age `medvars' m_age `fedvars' f_age `famvars' year2007  if prob_work_m >= `p_work_min_m' & prob_work_m !=.

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:ln_wage_m_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      928}}}{space 1}{space 1}{ralign 9:{res:{sf:      928}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.487983}}}{space 1}{space 1}{ralign 9:{res:{sf: .3874064}}}{space 1}{space 1}{ralign 9:{res:{sf: .6224198}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.483311}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.991448}}}{space 1}{space 1}{ralign 9:{res:{sf: 2308.848}}}{space 1}
{space 0}{space 0}{ralign 12:ln_wage_f_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      662}}}{space 1}{space 1}{ralign 9:{res:{sf:      662}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.977726}}}{space 1}{space 1}{ralign 9:{res:{sf: .3568543}}}{space 1}{space 1}{ralign 9:{res:{sf: .5973728}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.251684}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.904192}}}{space 1}{space 1}{ralign 9:{res:{sf: 1971.254}}}{space 1}
{space 0}{space 0}{ralign 12:ln_pratio_~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.101782}}}{space 1}{space 1}{ralign 9:{res:{sf: .1030537}}}{space 1}{space 1}{ralign 9:{res:{sf: .3210198}}}{space 1}{space 1}{ralign 9:{res:{sf: .2739018}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.891691}}}{space 1}{space 1}{ralign 9:{res:{sf:  1273.66}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.595156}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.209985}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.051825}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:       12}}}{space 1}{space 1}{ralign 9:{res:{sf:    11092}}}{space 1}
{space 0}{space 0}{ralign 12:med_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf: .3062284}}}{space 1}{space 1}{ralign 9:{res:{sf: .2126365}}}{space 1}{space 1}{ralign 9:{res:{sf: .4611253}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      354}}}{space 1}
{space 0}{space 0}{ralign 12:med_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf: .3512111}}}{space 1}{space 1}{ralign 9:{res:{sf: .2280591}}}{space 1}{space 1}{ralign 9:{res:{sf: .4775554}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      406}}}{space 1}
{space 0}{space 0}{ralign 12:med_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf: .3269896}}}{space 1}{space 1}{ralign 9:{res:{sf: .2202579}}}{space 1}{space 1}{ralign 9:{res:{sf: .4693165}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      378}}}{space 1}
{space 0}{space 0}{ralign 12:m_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf: 37.87457}}}{space 1}{space 1}{ralign 9:{res:{sf: 40.49767}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.363778}}}{space 1}{space 1}{ralign 9:{res:{sf:       21}}}{space 1}{space 1}{ralign 9:{res:{sf:       55}}}{space 1}{space 1}{ralign 9:{res:{sf:    43783}}}{space 1}
{space 0}{space 0}{ralign 12:fed_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      744}}}{space 1}{space 1}{ralign 9:{res:{sf:      744}}}{space 1}{space 1}{ralign 9:{res:{sf: .3991935}}}{space 1}{space 1}{ralign 9:{res:{sf: .2401609}}}{space 1}{space 1}{ralign 9:{res:{sf: .4900621}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      297}}}{space 1}
{space 0}{space 0}{ralign 12:fed_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      744}}}{space 1}{space 1}{ralign 9:{res:{sf:      744}}}{space 1}{space 1}{ralign 9:{res:{sf: .2647849}}}{space 1}{space 1}{ralign 9:{res:{sf: .1949359}}}{space 1}{space 1}{ralign 9:{res:{sf: .4415154}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      197}}}{space 1}
{space 0}{space 0}{ralign 12:fed_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      744}}}{space 1}{space 1}{ralign 9:{res:{sf:      744}}}{space 1}{space 1}{ralign 9:{res:{sf: .3172043}}}{space 1}{space 1}{ralign 9:{res:{sf: .2168772}}}{space 1}{space 1}{ralign 9:{res:{sf: .4657008}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      236}}}{space 1}
{space 0}{space 0}{ralign 12:f_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      744}}}{space 1}{space 1}{ralign 9:{res:{sf:      744}}}{space 1}{space 1}{ralign 9:{res:{sf:  40.7164}}}{space 1}{space 1}{ralign 9:{res:{sf: 48.83602}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.988277}}}{space 1}{space 1}{ralign 9:{res:{sf:       20}}}{space 1}{space 1}{ralign 9:{res:{sf:       64}}}{space 1}{space 1}{ralign 9:{res:{sf:    30293}}}{space 1}
{space 0}{space 0}{ralign 12:m_white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf: .5692042}}}{space 1}{space 1}{ralign 9:{res:{sf: .2454231}}}{space 1}{space 1}{ralign 9:{res:{sf: .4954019}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      658}}}{space 1}
{space 0}{space 0}{ralign 12:num_0_5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf: .1410035}}}{space 1}{space 1}{ralign 9:{res:{sf: .1368108}}}{space 1}{space 1}{ralign 9:{res:{sf: .3698794}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:      163}}}{space 1}
{space 0}{space 0}{ralign 12:num_child}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.977509}}}{space 1}{space 1}{ralign 9:{res:{sf: .4964634}}}{space 1}{space 1}{ralign 9:{res:{sf: .7046016}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        6}}}{space 1}{space 1}{ralign 9:{res:{sf:     2286}}}{space 1}
{space 0}{space 0}{ralign 12:year2007}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:     1156}}}{space 1}{space 1}{ralign 9:{res:{sf:  .199827}}}{space 1}{space 1}{ralign 9:{res:{sf: .1600346}}}{space 1}{space 1}{ralign 9:{res:{sf: .4000433}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      231}}}{space 1}

{com}. 
. disp "Summary statistics for sample with positive childcare expenditures: 2002 and 2007"        
{res}Summary statistics for sample with positive childcare expenditures: 2002 and 2007
{txt}
{com}. eststo t_rest_chcare: estpost summ ln_wage_m_rel ln_wage_f_rel ln_pratio_4ca age `medvars' m_age `fedvars' f_age `famvars' year2007 if `pccare'==1

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:ln_wage_m_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      384}}}{space 1}{space 1}{ralign 9:{res:{sf:      384}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.564568}}}{space 1}{space 1}{ralign 9:{res:{sf:  .270563}}}{space 1}{space 1}{ralign 9:{res:{sf: .5201567}}}{space 1}{space 1}{ralign 9:{res:{sf: .6882275}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.886202}}}{space 1}{space 1}{ralign 9:{res:{sf: 984.7941}}}{space 1}
{space 0}{space 0}{ralign 12:ln_wage_f_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      247}}}{space 1}{space 1}{ralign 9:{res:{sf:      247}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.861619}}}{space 1}{space 1}{ralign 9:{res:{sf: .2768667}}}{space 1}{space 1}{ralign 9:{res:{sf: .5261813}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.251684}}}{space 1}{space 1}{ralign 9:{res:{sf:  4.55071}}}{space 1}{space 1}{ralign 9:{res:{sf: 706.8198}}}{space 1}
{space 0}{space 0}{ralign 12:ln_pratio_~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.104381}}}{space 1}{space 1}{ralign 9:{res:{sf: .1073398}}}{space 1}{space 1}{ralign 9:{res:{sf: .3276275}}}{space 1}{space 1}{ralign 9:{res:{sf: .2739018}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.891691}}}{space 1}{space 1}{ralign 9:{res:{sf: 467.1531}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.335697}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.806471}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.951018}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:       12}}}{space 1}{space 1}{ralign 9:{res:{sf:     3526}}}{space 1}
{space 0}{space 0}{ralign 12:med_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      422}}}{space 1}{space 1}{ralign 9:{res:{sf:      422}}}{space 1}{space 1}{ralign 9:{res:{sf: .2511848}}}{space 1}{space 1}{ralign 9:{res:{sf: .1885378}}}{space 1}{space 1}{ralign 9:{res:{sf: .4342094}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      106}}}{space 1}
{space 0}{space 0}{ralign 12:med_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      422}}}{space 1}{space 1}{ralign 9:{res:{sf:      422}}}{space 1}{space 1}{ralign 9:{res:{sf: .3720379}}}{space 1}{space 1}{ralign 9:{res:{sf: .2341806}}}{space 1}{space 1}{ralign 9:{res:{sf: .4839221}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      157}}}{space 1}
{space 0}{space 0}{ralign 12:med_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      422}}}{space 1}{space 1}{ralign 9:{res:{sf:      422}}}{space 1}{space 1}{ralign 9:{res:{sf: .3317536}}}{space 1}{space 1}{ralign 9:{res:{sf: .2222197}}}{space 1}{space 1}{ralign 9:{res:{sf: .4714019}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      140}}}{space 1}
{space 0}{space 0}{ralign 12:m_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf: 35.92199}}}{space 1}{space 1}{ralign 9:{res:{sf: 38.67873}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.219223}}}{space 1}{space 1}{ralign 9:{res:{sf:       23}}}{space 1}{space 1}{ralign 9:{res:{sf:       53}}}{space 1}{space 1}{ralign 9:{res:{sf:    15195}}}{space 1}
{space 0}{space 0}{ralign 12:fed_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      265}}}{space 1}{space 1}{ralign 9:{res:{sf:      265}}}{space 1}{space 1}{ralign 9:{res:{sf:  .354717}}}{space 1}{space 1}{ralign 9:{res:{sf: .2297599}}}{space 1}{space 1}{ralign 9:{res:{sf: .4793327}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       94}}}{space 1}
{space 0}{space 0}{ralign 12:fed_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      265}}}{space 1}{space 1}{ralign 9:{res:{sf:      265}}}{space 1}{space 1}{ralign 9:{res:{sf: .2264151}}}{space 1}{space 1}{ralign 9:{res:{sf: .1758148}}}{space 1}{space 1}{ralign 9:{res:{sf: .4193027}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       60}}}{space 1}
{space 0}{space 0}{ralign 12:fed_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      265}}}{space 1}{space 1}{ralign 9:{res:{sf:      265}}}{space 1}{space 1}{ralign 9:{res:{sf: .3433962}}}{space 1}{space 1}{ralign 9:{res:{sf: .2263293}}}{space 1}{space 1}{ralign 9:{res:{sf: .4757408}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       91}}}{space 1}
{space 0}{space 0}{ralign 12:f_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      264}}}{space 1}{space 1}{ralign 9:{res:{sf:      264}}}{space 1}{space 1}{ralign 9:{res:{sf: 38.71212}}}{space 1}{space 1}{ralign 9:{res:{sf: 45.32365}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.732285}}}{space 1}{space 1}{ralign 9:{res:{sf:       20}}}{space 1}{space 1}{ralign 9:{res:{sf:       55}}}{space 1}{space 1}{ralign 9:{res:{sf:    10220}}}{space 1}
{space 0}{space 0}{ralign 12:m_white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      421}}}{space 1}{space 1}{ralign 9:{res:{sf:      421}}}{space 1}{space 1}{ralign 9:{res:{sf: .5581948}}}{space 1}{space 1}{ralign 9:{res:{sf: .2472005}}}{space 1}{space 1}{ralign 9:{res:{sf: .4971927}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      235}}}{space 1}
{space 0}{space 0}{ralign 12:num_0_5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf: .3404255}}}{space 1}{space 1}{ralign 9:{res:{sf: .2535041}}}{space 1}{space 1}{ralign 9:{res:{sf: .5034919}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:      144}}}{space 1}
{space 0}{space 0}{ralign 12:num_child}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.865248}}}{space 1}{space 1}{ralign 9:{res:{sf: .3727942}}}{space 1}{space 1}{ralign 9:{res:{sf: .6105687}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:      789}}}{space 1}
{space 0}{space 0}{ralign 12:year2007}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf:      423}}}{space 1}{space 1}{ralign 9:{res:{sf: .0425532}}}{space 1}{space 1}{ralign 9:{res:{sf:  .040839}}}{space 1}{space 1}{ralign 9:{res:{sf: .2020865}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       18}}}{space 1}

{com}. 
. *  Sample used in table 2
. preserve
{txt}
{com}. keep if prob_work_m >= `p_work_min_m' & prob_work_m !=.
{txt}(356 observations deleted)

{com}. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm4'

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       727
{txt}{hline 13}{c +}{hline 34}   F(10, 716)      = {res}    16.76
{txt}       Model {c |} {res} 193.573683        10  19.3573683   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 826.794496       716  1.15474092   {txt}R-squared       ={res}    0.1897
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1784
{txt}       Total {c |} {res} 1020.36818       726  1.40546581   {txt}Root MSE        =   {res} 1.0746

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6452337{col 27}{space 2}  .071377{col 38}{space 1}    9.04{col 47}{space 3}0.000{col 55}{space 4} .5051004{col 68}{space 3}  .785367
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0750842{col 27}{space 2} .0951348{col 38}{space 1}   -0.79{col 47}{space 3}0.430{col 55}{space 4}-.2618606{col 68}{space 3} .1116923
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1413157{col 27}{space 2} .0222791{col 38}{space 1}   -6.34{col 47}{space 3}0.000{col 55}{space 4}-.1850559{col 68}{space 3}-.0975755
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2} .0992183{col 27}{space 2} .3501145{col 38}{space 1}    0.28{col 47}{space 3}0.777{col 55}{space 4}-.5881555{col 68}{space 3}  .786592
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1061078{col 27}{space 2}    .3506{col 38}{space 1}    0.30{col 47}{space 3}0.762{col 55}{space 4}-.5822191{col 68}{space 3} .7944347
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} -.061088{col 27}{space 2} .3570282{col 38}{space 1}   -0.17{col 47}{space 3}0.864{col 55}{space 4}-.7620354{col 68}{space 3} .6398594
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0079797{col 27}{space 2} .0075507{col 38}{space 1}   -1.06{col 47}{space 3}0.291{col 55}{space 4}-.0228039{col 68}{space 3} .0068444
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2436214{col 27}{space 2} .0895959{col 38}{space 1}   -2.72{col 47}{space 3}0.007{col 55}{space 4}-.4195235{col 68}{space 3}-.0677192
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1564121{col 27}{space 2}  .125695{col 38}{space 1}    1.24{col 47}{space 3}0.214{col 55}{space 4}-.0903627{col 68}{space 3} .4031869
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0891213{col 27}{space 2} .0615566{col 38}{space 1}    1.45{col 47}{space 3}0.148{col 55}{space 4}-.0317317{col 68}{space 3} .2099742
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.126445{col 27}{space 2} .4687495{col 38}{space 1}    4.54{col 47}{space 3}0.000{col 55}{space 4} 1.206157{col 68}{space 3} 3.046733
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. disp "Summary statistics for sample used in table 2"    
{res}Summary statistics for sample used in table 2
{txt}
{com}. eststo t_rest_table2: estpost summ ln_wage_m_rel ln_wage_f_rel ln_pratio_4ca age `medvars' m_age `fedvars' f_age `famvars' year2007 if e(sample)==1

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:ln_wage_m_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.511398}}}{space 1}{space 1}{ralign 9:{res:{sf: .3858415}}}{space 1}{space 1}{ralign 9:{res:{sf: .6211614}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.483311}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.991448}}}{space 1}{space 1}{ralign 9:{res:{sf: 1825.786}}}{space 1}
{space 0}{space 0}{ralign 12:ln_wage_f_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      451}}}{space 1}{space 1}{ralign 9:{res:{sf:      451}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.956177}}}{space 1}{space 1}{ralign 9:{res:{sf: .3171142}}}{space 1}{space 1}{ralign 9:{res:{sf: .5631289}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.251684}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.904192}}}{space 1}{space 1}{ralign 9:{res:{sf: 1333.236}}}{space 1}
{space 0}{space 0}{ralign 12:ln_pratio_~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.103385}}}{space 1}{space 1}{ralign 9:{res:{sf: .1022652}}}{space 1}{space 1}{ralign 9:{res:{sf: .3197893}}}{space 1}{space 1}{ralign 9:{res:{sf: .2739018}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.891691}}}{space 1}{space 1}{ralign 9:{res:{sf: 802.1606}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.638239}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.024596}}}{space 1}{space 1}{ralign 9:{res:{sf:  2.00614}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:       12}}}{space 1}{space 1}{ralign 9:{res:{sf:     7007}}}{space 1}
{space 0}{space 0}{ralign 12:med_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: .2874828}}}{space 1}{space 1}{ralign 9:{res:{sf: .2051186}}}{space 1}{space 1}{ralign 9:{res:{sf: .4529002}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      209}}}{space 1}
{space 0}{space 0}{ralign 12:med_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: .3425034}}}{space 1}{space 1}{ralign 9:{res:{sf:  .225505}}}{space 1}{space 1}{ralign 9:{res:{sf: .4748737}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      249}}}{space 1}
{space 0}{space 0}{ralign 12:med_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: .3562586}}}{space 1}{space 1}{ralign 9:{res:{sf: .2296543}}}{space 1}{space 1}{ralign 9:{res:{sf: .4792226}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      259}}}{space 1}
{space 0}{space 0}{ralign 12:m_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: 37.92022}}}{space 1}{space 1}{ralign 9:{res:{sf: 39.46195}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.281874}}}{space 1}{space 1}{ralign 9:{res:{sf:       23}}}{space 1}{space 1}{ralign 9:{res:{sf:       55}}}{space 1}{space 1}{ralign 9:{res:{sf:    27568}}}{space 1}
{space 0}{space 0}{ralign 12:fed_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      491}}}{space 1}{space 1}{ralign 9:{res:{sf:      491}}}{space 1}{space 1}{ralign 9:{res:{sf: .4154786}}}{space 1}{space 1}{ralign 9:{res:{sf: .2433518}}}{space 1}{space 1}{ralign 9:{res:{sf:  .493307}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      204}}}{space 1}
{space 0}{space 0}{ralign 12:fed_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      491}}}{space 1}{space 1}{ralign 9:{res:{sf:      491}}}{space 1}{space 1}{ralign 9:{res:{sf: .2668024}}}{space 1}{space 1}{ralign 9:{res:{sf: .1960181}}}{space 1}{space 1}{ralign 9:{res:{sf: .4427393}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      131}}}{space 1}
{space 0}{space 0}{ralign 12:fed_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      491}}}{space 1}{space 1}{ralign 9:{res:{sf:      491}}}{space 1}{space 1}{ralign 9:{res:{sf: .2973523}}}{space 1}{space 1}{ralign 9:{res:{sf: .2093603}}}{space 1}{space 1}{ralign 9:{res:{sf: .4575591}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      146}}}{space 1}
{space 0}{space 0}{ralign 12:f_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      491}}}{space 1}{space 1}{ralign 9:{res:{sf:      491}}}{space 1}{space 1}{ralign 9:{res:{sf: 40.50509}}}{space 1}{space 1}{ralign 9:{res:{sf:  47.0015}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.855764}}}{space 1}{space 1}{ralign 9:{res:{sf:       20}}}{space 1}{space 1}{ralign 9:{res:{sf:       60}}}{space 1}{space 1}{ralign 9:{res:{sf:    19888}}}{space 1}
{space 0}{space 0}{ralign 12:m_white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: .5928473}}}{space 1}{space 1}{ralign 9:{res:{sf: .2417119}}}{space 1}{space 1}{ralign 9:{res:{sf:  .491642}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      431}}}{space 1}
{space 0}{space 0}{ralign 12:num_0_5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:  .133425}}}{space 1}{space 1}{ralign 9:{res:{sf: .1295562}}}{space 1}{space 1}{ralign 9:{res:{sf: .3599391}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:       97}}}{space 1}
{space 0}{space 0}{ralign 12:num_child}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.966988}}}{space 1}{space 1}{ralign 9:{res:{sf:   .43968}}}{space 1}{space 1}{ralign 9:{res:{sf: .6630837}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:     1430}}}{space 1}
{space 0}{space 0}{ralign 12:year2007}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf:      727}}}{space 1}{space 1}{ralign 9:{res:{sf: .1898212}}}{space 1}{space 1}{ralign 9:{res:{sf: .1540009}}}{space 1}{space 1}{ralign 9:{res:{sf: .3924295}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      138}}}{space 1}

{com}. 
. * Sample used in table 4
. restore
{txt}
{com}. foreach pvar in 4ca {c -(}
{txt}  2{com}. local zval= 5 
{txt}  3{com}.                 regress ln_invratio_imp  ln_pratio_`pvar' `Zall`zval''
{txt}  4{com}. {c )-}

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       347
{txt}{hline 13}{c +}{hline 34}   F(12, 334)      = {res}     3.99
{txt}       Model {c |} {res} 65.1093226        12  5.42577688   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 453.779728       334  1.35862194   {txt}R-squared       ={res}    0.1255
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0941
{txt}       Total {c |} {res}  518.88905       346  1.49967934   {txt}Root MSE        =   {res} 1.1656

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .6498913{col 30}{space 2} .2042021{col 41}{space 1}    3.18{col 50}{space 3}0.002{col 58}{space 4} .2482069{col 71}{space 3} 1.051576
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2}  .834789{col 30}{space 2} .5729723{col 41}{space 1}    1.46{col 50}{space 3}0.146{col 58}{space 4}-.2923001{col 71}{space 3} 1.961878
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.1235225{col 30}{space 2} .0357705{col 41}{space 1}   -3.45{col 50}{space 3}0.001{col 58}{space 4}-.1938863{col 71}{space 3}-.0531587
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2}-.0545783{col 30}{space 2} .1614961{col 41}{space 1}   -0.34{col 50}{space 3}0.736{col 58}{space 4}-.3722561{col 71}{space 3} .2630994
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.3153154{col 30}{space 2} .1690761{col 41}{space 1}   -1.86{col 50}{space 3}0.063{col 58}{space 4}-.6479037{col 71}{space 3} .0172728
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0023736{col 30}{space 2} .0133993{col 41}{space 1}    0.18{col 50}{space 3}0.860{col 58}{space 4}-.0239839{col 71}{space 3} .0287312
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .1024446{col 30}{space 2} .2081371{col 41}{space 1}    0.49{col 50}{space 3}0.623{col 58}{space 4}-.3069802{col 71}{space 3} .5118693
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.4352918{col 30}{space 2} .2190415{col 41}{space 1}   -1.99{col 50}{space 3}0.048{col 58}{space 4}-.8661666{col 71}{space 3}-.0044171
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2} -.017108{col 30}{space 2} .0148714{col 41}{space 1}   -1.15{col 50}{space 3}0.251{col 58}{space 4}-.0463614{col 71}{space 3} .0121455
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.3281448{col 30}{space 2}  .139894{col 41}{space 1}   -2.35{col 50}{space 3}0.020{col 58}{space 4}-.6033293{col 71}{space 3}-.0529604
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2}-.0304874{col 30}{space 2} .1430166{col 41}{space 1}   -0.21{col 50}{space 3}0.831{col 58}{space 4}-.3118143{col 71}{space 3} .2508395
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .0954052{col 30}{space 2}  .111848{col 41}{space 1}    0.85{col 50}{space 3}0.394{col 58}{space 4}-.1246101{col 71}{space 3} .3154205
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .9330048{col 30}{space 2} .5907579{col 41}{space 1}    1.58{col 50}{space 3}0.115{col 58}{space 4}-.2290703{col 71}{space 3}  2.09508
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. disp "Summary statistics for sample used in table 4"    
{res}Summary statistics for sample used in table 4
{txt}
{com}. eststo t_rest_table4: estpost summ ln_wage_m_rel ln_wage_f_rel ln_pratio_4ca age `medvars' m_age `fedvars' f_age `famvars' year2007  if e(sample)==1

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:ln_wage_m_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      322}}}{space 1}{space 1}{ralign 9:{res:{sf:      322}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.579479}}}{space 1}{space 1}{ralign 9:{res:{sf: .2810833}}}{space 1}{space 1}{ralign 9:{res:{sf: .5301729}}}{space 1}{space 1}{ralign 9:{res:{sf: .6882275}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.886202}}}{space 1}{space 1}{ralign 9:{res:{sf: 830.5922}}}{space 1}
{space 0}{space 0}{ralign 12:ln_wage_f_~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      213}}}{space 1}{space 1}{ralign 9:{res:{sf:      213}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.860402}}}{space 1}{space 1}{ralign 9:{res:{sf: .2651969}}}{space 1}{space 1}{ralign 9:{res:{sf: .5149727}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.251684}}}{space 1}{space 1}{ralign 9:{res:{sf:  4.55071}}}{space 1}{space 1}{ralign 9:{res:{sf: 609.2657}}}{space 1}
{space 0}{space 0}{ralign 12:ln_pratio_~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.095425}}}{space 1}{space 1}{ralign 9:{res:{sf: .1064524}}}{space 1}{space 1}{ralign 9:{res:{sf: .3262704}}}{space 1}{space 1}{ralign 9:{res:{sf: .2739018}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.891691}}}{space 1}{space 1}{ralign 9:{res:{sf: 380.1123}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.360231}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.820726}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.954668}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:       12}}}{space 1}{space 1}{ralign 9:{res:{sf:     2901}}}{space 1}
{space 0}{space 0}{ralign 12:med_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: .2536023}}}{space 1}{space 1}{ralign 9:{res:{sf: .1898353}}}{space 1}{space 1}{ralign 9:{res:{sf: .4357009}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       88}}}{space 1}
{space 0}{space 0}{ralign 12:med_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: .3487032}}}{space 1}{space 1}{ralign 9:{res:{sf: .2277657}}}{space 1}{space 1}{ralign 9:{res:{sf:  .477248}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      121}}}{space 1}
{space 0}{space 0}{ralign 12:med_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: .3544669}}}{space 1}{space 1}{ralign 9:{res:{sf: .2294814}}}{space 1}{space 1}{ralign 9:{res:{sf: .4790422}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      123}}}{space 1}
{space 0}{space 0}{ralign 12:m_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: 35.99712}}}{space 1}{space 1}{ralign 9:{res:{sf: 40.44797}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.359872}}}{space 1}{space 1}{ralign 9:{res:{sf:       23}}}{space 1}{space 1}{ralign 9:{res:{sf:       53}}}{space 1}{space 1}{ralign 9:{res:{sf:    12491}}}{space 1}
{space 0}{space 0}{ralign 12:fed_hs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      227}}}{space 1}{space 1}{ralign 9:{res:{sf:      227}}}{space 1}{space 1}{ralign 9:{res:{sf: .3524229}}}{space 1}{space 1}{ralign 9:{res:{sf: .2292308}}}{space 1}{space 1}{ralign 9:{res:{sf: .4787806}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       80}}}{space 1}
{space 0}{space 0}{ralign 12:fed_scoll}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      227}}}{space 1}{space 1}{ralign 9:{res:{sf:      227}}}{space 1}{space 1}{ralign 9:{res:{sf: .2246696}}}{space 1}{space 1}{ralign 9:{res:{sf: .1749639}}}{space 1}{space 1}{ralign 9:{res:{sf: .4182869}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       51}}}{space 1}
{space 0}{space 0}{ralign 12:fed_collplus}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      227}}}{space 1}{space 1}{ralign 9:{res:{sf:      227}}}{space 1}{space 1}{ralign 9:{res:{sf: .3480176}}}{space 1}{space 1}{ralign 9:{res:{sf: .2279053}}}{space 1}{space 1}{ralign 9:{res:{sf: .4773943}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       79}}}{space 1}
{space 0}{space 0}{ralign 12:f_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      227}}}{space 1}{space 1}{ralign 9:{res:{sf:      227}}}{space 1}{space 1}{ralign 9:{res:{sf: 38.74009}}}{space 1}{space 1}{ralign 9:{res:{sf: 46.75073}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.837451}}}{space 1}{space 1}{ralign 9:{res:{sf:       20}}}{space 1}{space 1}{ralign 9:{res:{sf:       55}}}{space 1}{space 1}{ralign 9:{res:{sf:     8794}}}{space 1}
{space 0}{space 0}{ralign 12:m_white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: .5619597}}}{space 1}{space 1}{ralign 9:{res:{sf: .2468724}}}{space 1}{space 1}{ralign 9:{res:{sf: .4968626}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      195}}}{space 1}
{space 0}{space 0}{ralign 12:num_0_5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: .3371758}}}{space 1}{space 1}{ralign 9:{res:{sf: .2530359}}}{space 1}{space 1}{ralign 9:{res:{sf: .5030268}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:      117}}}{space 1}
{space 0}{space 0}{ralign 12:num_child}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.873199}}}{space 1}{space 1}{ralign 9:{res:{sf: .3364762}}}{space 1}{space 1}{ralign 9:{res:{sf: .5800656}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:      650}}}{space 1}
{space 0}{space 0}{ralign 12:year2007}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf:      347}}}{space 1}{space 1}{ralign 9:{res:{sf: .0489914}}}{space 1}{space 1}{ralign 9:{res:{sf: .0467259}}}{space 1}{space 1}{ralign 9:{res:{sf: .2161616}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       17}}}{space 1}

{com}. 
. 
. *************************Residual Variances for MC analysis*****************************************
. 
. preserve
{txt}
{com}. * Use sample corresponding to Table 2 Column 2
. keep if prob_work_m >= `p_work_min_m' & prob_work_m !=.
{txt}(356 observations deleted)

{com}. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5'

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       727
{txt}{hline 13}{c +}{hline 34}   F(9, 717)       = {res}    18.64
{txt}       Model {c |} {res} 193.480948         9  21.4978831   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 826.887232       717  1.15325974   {txt}R-squared       ={res}    0.1896
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1794
{txt}       Total {c |} {res} 1020.36818       726  1.40546581   {txt}Root MSE        =   {res} 1.0739

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6461114{col 27}{space 2}  .071264{col 38}{space 1}    9.07{col 47}{space 3}0.000{col 55}{space 4} .5062003{col 68}{space 3} .7860226
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0737061{col 27}{space 2} .0949495{col 38}{space 1}   -0.78{col 47}{space 3}0.438{col 55}{space 4}-.2601183{col 68}{space 3} .1127061
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1410849{col 27}{space 2} .0222499{col 38}{space 1}   -6.34{col 47}{space 3}0.000{col 55}{space 4}-.1847677{col 68}{space 3}-.0974021
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0110723{col 27}{space 2} .1021967{col 38}{space 1}    0.11{col 47}{space 3}0.914{col 55}{space 4}-.1895683{col 68}{space 3} .2117129
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1571411{col 27}{space 2} .1121116{col 38}{space 1}   -1.40{col 47}{space 3}0.161{col 55}{space 4}-.3772474{col 68}{space 3} .0629652
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} -.007913{col 27}{space 2} .0075422{col 38}{space 1}   -1.05{col 47}{space 3}0.294{col 55}{space 4}-.0227205{col 68}{space 3} .0068944
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2426265{col 27}{space 2} .0894697{col 38}{space 1}   -2.71{col 47}{space 3}0.007{col 55}{space 4}-.4182804{col 68}{space 3}-.0669726
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1582784{col 27}{space 2} .1254418{col 38}{space 1}    1.26{col 47}{space 3}0.207{col 55}{space 4}-.0879988{col 68}{space 3} .4045556
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}  .089088{col 27}{space 2}  .061517{col 38}{space 1}    1.45{col 47}{space 3}0.148{col 55}{space 4}-.0316869{col 68}{space 3} .2098629
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.213075{col 27}{space 2} .3551268{col 38}{space 1}    6.23{col 47}{space 3}0.000{col 55}{space 4} 1.515863{col 68}{space 3} 2.910288
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. generate sample_dummy=1 if e(sample)==1
{txt}(429 missing values generated)

{com}. replace sample_dummy=0 if sample_dummy==.
{txt}(429 real changes made)

{com}. * Var(zeta)
. display e(rss)/(e(N)-1)
{res}1.1389631
{txt}
{com}. 
. * Var(ln(pi))
. regress ln_wage_m_rel curr_married `Zm5' if sample_dummy==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       727
{txt}{hline 13}{c +}{hline 34}   F(8, 718)       = {res}    20.96
{txt}       Model {c |} {res}  53.037124         8   6.6296405   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 227.083832       718  .316272747   {txt}R-squared       ={res}    0.1893
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1803
{txt}       Total {c |} {res} 280.120956       726  .385841538   {txt}Root MSE        =   {res} .56238

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_wage_m_~l{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
curr_married {c |}{col 14}{res}{space 2}-.0477304{col 26}{space 2} .0496914{col 37}{space 1}   -0.96{col 46}{space 3}0.337{col 54}{space 4}-.1452881{col 67}{space 3} .0498274
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0175581{col 26}{space 2} .0116334{col 37}{space 1}   -1.51{col 46}{space 3}0.132{col 54}{space 4}-.0403978{col 67}{space 3} .0052815
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2}  .281571{col 26}{space 2} .0524768{col 37}{space 1}    5.37{col 46}{space 3}0.000{col 54}{space 4} .1785447{col 67}{space 3} .3845973
{txt}med_collplus {c |}{col 14}{res}{space 2} .5888909{col 26}{space 2} .0544422{col 37}{space 1}   10.82{col 46}{space 3}0.000{col 54}{space 4} .4820059{col 67}{space 3}  .695776
{txt}{space 7}m_age {c |}{col 14}{res}{space 2} .0115205{col 26}{space 2} .0039262{col 37}{space 1}    2.93{col 46}{space 3}0.003{col 54}{space 4} .0038122{col 67}{space 3} .0192287
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .0656311{col 26}{space 2} .0467896{col 37}{space 1}    1.40{col 46}{space 3}0.161{col 54}{space 4}-.0262297{col 67}{space 3} .1574918
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2} .0286369{col 26}{space 2} .0656829{col 37}{space 1}    0.44{col 46}{space 3}0.663{col 54}{space 4}-.1003165{col 67}{space 3} .1575904
{txt}{space 3}num_child {c |}{col 14}{res}{space 2}-.0801953{col 26}{space 2}  .032076{col 37}{space 1}   -2.50{col 46}{space 3}0.013{col 54}{space 4}-.1431693{col 67}{space 3}-.0172214
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.084782{col 26}{space 2} .1689164{col 37}{space 1}   12.34{col 46}{space 3}0.000{col 54}{space 4} 1.753153{col 67}{space 3} 2.416411
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. display e(rss)/(e(N)-1)
{res}.31278765
{txt}
{com}. 
. * Var(ln(x_1))
. generate ln_g=ln_hhinvest-ln(p_avg)
{txt}(102 missing values generated)

{com}. regress ln_g curr_married `Zm5' if sample_dummy==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       727
{txt}{hline 13}{c +}{hline 34}   F(8, 718)       = {res}    21.29
{txt}       Model {c |} {res} 112.666266         8  14.0832833   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 474.987501       718  .661542481   {txt}R-squared       ={res}    0.1917
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1827
{txt}       Total {c |} {res} 587.653768       726  .809440451   {txt}Root MSE        =   {res} .81335

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        ln_g{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
curr_married {c |}{col 14}{res}{space 2}   .08832{col 26}{space 2} .0718669{col 37}{space 1}    1.23{col 46}{space 3}0.219{col 54}{space 4}-.0527743{col 67}{space 3} .2294144
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0661177{col 26}{space 2}  .016825{col 37}{space 1}    3.93{col 46}{space 3}0.000{col 54}{space 4} .0330856{col 67}{space 3} .0991499
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2} .1866875{col 26}{space 2} .0758954{col 37}{space 1}    2.46{col 46}{space 3}0.014{col 54}{space 4} .0376841{col 67}{space 3} .3356908
{txt}med_collplus {c |}{col 14}{res}{space 2} .4781504{col 26}{space 2} .0787379{col 37}{space 1}    6.07{col 46}{space 3}0.000{col 54}{space 4} .3235663{col 67}{space 3} .6327345
{txt}{space 7}m_age {c |}{col 14}{res}{space 2} .0111609{col 26}{space 2} .0056784{col 37}{space 1}    1.97{col 46}{space 3}0.050{col 54}{space 4} .0000127{col 67}{space 3} .0223091
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .4284221{col 26}{space 2} .0676701{col 37}{space 1}    6.33{col 46}{space 3}0.000{col 54}{space 4} .2955671{col 67}{space 3} .5612771
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2}-.0167292{col 26}{space 2} .0949949{col 37}{space 1}   -0.18{col 46}{space 3}0.860{col 54}{space 4}-.2032301{col 67}{space 3} .1697717
{txt}{space 3}num_child {c |}{col 14}{res}{space 2} -.168392{col 26}{space 2} .0463904{col 37}{space 1}   -3.63{col 46}{space 3}0.000{col 54}{space 4} -.259469{col 67}{space 3}-.0773151
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.300043{col 26}{space 2} .2442979{col 37}{space 1}    5.32{col 46}{space 3}0.000{col 54}{space 4} .8204196{col 67}{space 3} 1.779667
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. predict res_g, residuals
{txt}(102 missing values generated)

{com}. replace res_g=. if sample_dummy==0
{txt}(327 real changes made, 327 to missing)

{com}. display e(rss)/(e(N)-1)
{res}.65425276
{txt}
{com}. 
. * Correlations of residuals
. regress ln_tau_m curr_married `Zm5' if sample_dummy==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       727
{txt}{hline 13}{c +}{hline 34}   F(8, 718)       = {res}     5.65
{txt}       Model {c |} {res}  28.314701         8  3.53933762   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 449.475325       718  .626010202   {txt}R-squared       ={res}    0.0593
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0488
{txt}       Total {c |} {res} 477.790026       726  .658112983   {txt}Root MSE        =   {res} .79121

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    ln_tau_m{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
curr_married {c |}{col 14}{res}{space 2} .0315052{col 26}{space 2} .0699102{col 37}{space 1}    0.45{col 46}{space 3}0.652{col 54}{space 4}-.1057477{col 67}{space 3} .1687581
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0687535{col 26}{space 2}  .016367{col 37}{space 1}   -4.20{col 46}{space 3}0.000{col 54}{space 4}-.1008863{col 67}{space 3}-.0366207
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2}  .098115{col 26}{space 2}  .073829{col 37}{space 1}    1.33{col 46}{space 3}0.184{col 54}{space 4}-.0468316{col 67}{space 3} .2430615
{txt}med_collplus {c |}{col 14}{res}{space 2} .1126075{col 26}{space 2} .0765942{col 37}{space 1}    1.47{col 46}{space 3}0.142{col 54}{space 4}-.0377678{col 67}{space 3} .2629829
{txt}{space 7}m_age {c |}{col 14}{res}{space 2}-.0008291{col 26}{space 2} .0055238{col 37}{space 1}   -0.15{col 46}{space 3}0.881{col 54}{space 4}-.0116738{col 67}{space 3} .0100156
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .1625695{col 26}{space 2} .0658277{col 37}{space 1}    2.47{col 46}{space 3}0.014{col 54}{space 4} .0333317{col 67}{space 3} .2918074
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2} .1314149{col 26}{space 2} .0924085{col 37}{space 1}    1.42{col 46}{space 3}0.155{col 54}{space 4}-.0500083{col 67}{space 3} .3128381
{txt}{space 3}num_child {c |}{col 14}{res}{space 2}-.0509239{col 26}{space 2} .0451273{col 37}{space 1}   -1.13{col 46}{space 3}0.260{col 54}{space 4}-.1395212{col 67}{space 3} .0376734
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.775338{col 26}{space 2} .2376466{col 37}{space 1}   11.68{col 46}{space 3}0.000{col 54}{space 4} 2.308773{col 67}{space 3} 3.241903
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. predict res_tau_m, residuals
{txt}(175 missing values generated)

{com}. replace res_tau_m=. if sample_dummy==0
{txt}(254 real changes made, 254 to missing)

{com}. 
. regress ln_tau_f curr_married `Zm5' if sample_dummy==1
{txt}note: curr_married omitted because of collinearity

      Source {c |}       SS           df       MS      Number of obs   ={res}       430
{txt}{hline 13}{c +}{hline 34}   F(7, 422)       = {res}     1.58
{txt}       Model {c |} {res} 8.73304122         7  1.24757732   {txt}Prob > F        ={res}    0.1404
{txt}    Residual {c |} {res}  333.90617       422  .791246849   {txt}R-squared       ={res}    0.0255
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0093
{txt}       Total {c |} {res} 342.639212       429  .798692801   {txt}Root MSE        =   {res} .88952

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    ln_tau_f{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
curr_married {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 9}age {c |}{col 14}{res}{space 2}-.0103206{col 26}{space 2} .0240283{col 37}{space 1}   -0.43{col 46}{space 3}0.668{col 54}{space 4}-.0575506{col 67}{space 3} .0369095
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2} .0994551{col 26}{space 2} .1162028{col 37}{space 1}    0.86{col 46}{space 3}0.393{col 54}{space 4}-.1289533{col 67}{space 3} .3278636
{txt}med_collplus {c |}{col 14}{res}{space 2} .1428969{col 26}{space 2} .1123789{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0779953{col 67}{space 3} .3637891
{txt}{space 7}m_age {c |}{col 14}{res}{space 2}-.0024369{col 26}{space 2}  .008332{col 37}{space 1}   -0.29{col 46}{space 3}0.770{col 54}{space 4}-.0188143{col 67}{space 3} .0139405
{txt}{space 5}m_white {c |}{col 14}{res}{space 2} .2530058{col 26}{space 2} .1008872{col 37}{space 1}    2.51{col 46}{space 3}0.013{col 54}{space 4} .0547017{col 67}{space 3} .4513099
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2} .1442171{col 26}{space 2} .1403073{col 37}{space 1}    1.03{col 46}{space 3}0.305{col 54}{space 4}-.1315711{col 67}{space 3} .4200053
{txt}{space 3}num_child {c |}{col 14}{res}{space 2}-.0645245{col 26}{space 2} .0704072{col 37}{space 1}   -0.92{col 46}{space 3}0.360{col 54}{space 4}-.2029169{col 67}{space 3} .0738679
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.919009{col 26}{space 2} .3702041{col 37}{space 1}    5.18{col 46}{space 3}0.000{col 54}{space 4} 1.191335{col 67}{space 3} 2.646683
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. predict res_tau_f, residuals
{txt}(578 missing values generated)

{com}. replace res_tau_f=. if sample_dummy==0
{txt}(148 real changes made, 148 to missing)

{com}. 
. generate ln_chcare_imp1=ln_chcare_imp-ln_pchcare_4ca
{txt}(815 missing values generated)

{com}. regress ln_chcare_imp1 curr_married `Zm5' if sample_dummy==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       238
{txt}{hline 13}{c +}{hline 34}   F(8, 229)       = {res}     1.48
{txt}       Model {c |} {res} 10.9951817         8  1.37439772   {txt}Prob > F        ={res}    0.1645
{txt}    Residual {c |} {res} 212.335981       229  .927231357   {txt}R-squared       ={res}    0.0492
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0160
{txt}       Total {c |} {res} 223.331162       237   .94232558   {txt}Root MSE        =   {res} .96293

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_chcare_~1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
curr_married {c |}{col 14}{res}{space 2}-.0248661{col 26}{space 2} .1366853{col 37}{space 1}   -0.18{col 46}{space 3}0.856{col 54}{space 4}-.2941877{col 67}{space 3} .2444554
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0251332{col 26}{space 2} .0366587{col 37}{space 1}   -0.69{col 46}{space 3}0.494{col 54}{space 4}-.0973648{col 67}{space 3} .0470983
{txt}{space 3}med_scoll {c |}{col 14}{res}{space 2} .1007578{col 26}{space 2} .1683304{col 37}{space 1}    0.60{col 46}{space 3}0.550{col 54}{space 4}-.2309166{col 67}{space 3} .4324321
{txt}med_collplus {c |}{col 14}{res}{space 2}  .383423{col 26}{space 2} .1700696{col 37}{space 1}    2.25{col 46}{space 3}0.025{col 54}{space 4} .0483218{col 67}{space 3} .7185242
{txt}{space 7}m_age {c |}{col 14}{res}{space 2} .0024201{col 26}{space 2}  .011253{col 37}{space 1}    0.22{col 46}{space 3}0.830{col 54}{space 4}-.0197526{col 67}{space 3} .0245928
{txt}{space 5}m_white {c |}{col 14}{res}{space 2}-.0957486{col 26}{space 2} .1305062{col 37}{space 1}   -0.73{col 46}{space 3}0.464{col 54}{space 4}-.3528951{col 67}{space 3} .1613979
{txt}{space 5}num_0_5 {c |}{col 14}{res}{space 2} .2261305{col 26}{space 2} .1517369{col 37}{space 1}    1.49{col 46}{space 3}0.138{col 54}{space 4}-.0728485{col 67}{space 3} .5251094
{txt}{space 3}num_child {c |}{col 14}{res}{space 2}-.0777202{col 26}{space 2} .1097537{col 37}{space 1}   -0.71{col 46}{space 3}0.480{col 54}{space 4}-.2939763{col 67}{space 3}  .138536
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.102022{col 26}{space 2} .4813369{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} 1.153607{col 67}{space 3} 3.050438
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. predict res_chcare, residuals
{txt}(815 missing values generated)

{com}. replace res_chcare=. if sample_dummy==0
{txt}(103 real changes made, 103 to missing)

{com}. 
. correlate res_g res_tau_m
{txt}(obs=727)

             {c |}    res_g res_ta~m
{hline 13}{c +}{hline 18}
       res_g {c |}{res}   1.0000
   {txt}res_tau_m {c |}{res}   0.0748   1.0000

{txt}
{com}. correlate res_g res_tau_m res_tau_f if curr_married==1
{txt}(obs=430)

             {c |}    res_g res_ta~m res_ta~f
{hline 13}{c +}{hline 27}
       res_g {c |}{res}   1.0000
   {txt}res_tau_m {c |}{res}   0.0690   1.0000
   {txt}res_tau_f {c |}{res}   0.1536   0.4761   1.0000

{txt}
{com}. correlate res_g res_chcare res_tau_m res_tau_f if curr_married==1
{txt}(obs=138)

             {c |}    res_g res_ch~e res_ta~m res_ta~f
{hline 13}{c +}{hline 36}
       res_g {c |}{res}   1.0000
  {txt}res_chcare {c |}{res}   0.0171   1.0000
   {txt}res_tau_m {c |}{res}   0.1815  -0.1607   1.0000
   {txt}res_tau_f {c |}{res}   0.2871  -0.1875   0.4103   1.0000

{txt}
{com}. correlate res_g res_tau_m if curr_married==0
{txt}(obs=236)

             {c |}    res_g res_ta~m
{hline 13}{c +}{hline 18}
       res_g {c |}{res}   1.0000
   {txt}res_tau_m {c |}{res}   0.0632   1.0000

{txt}
{com}. correlate res_g res_chcare res_tau_m if curr_married==0
{txt}(obs=87)

             {c |}    res_g res_ch~e res_ta~m
{hline 13}{c +}{hline 27}
       res_g {c |}{res}   1.0000
  {txt}res_chcare {c |}{res}   0.2878   1.0000
   {txt}res_tau_m {c |}{res}   0.0964  -0.0461   1.0000

{txt}
{com}. 
. restore
{txt}
{com}. 
. **************************************************************************************************
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Table 1: Summary statistics for full sample: 2002 and 2007
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> `'
{txt}
{com}. esttab  t_all using "$tables/all_summary.tex", booktabs  label nonumbers cells( "count(fmt(%2.0f) label(N)) mean(fmt(%8.2f) label(Mean)) sd(fmt(%8.2f) label(SD)) min(fmt(%8.2f) label(Min)) max(fmt(%8.2f) label(Max))")  ti(Summary statistics for full sample: 2002 and 2007) noobs  varwidth(15) modelwidth(11) wrap style(tex)  replace /*
> */  varlabels( ln_pratio_4ca "$\ln(\tilde{c -(}P{c )-}_{c -(}c,i{c )-})$" year2007 "Year 2007"   ln_wage_f_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}f{c )-})$"  ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll." med_scollplus "Mother some coll. + "  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children"  fed_hs "Father HS grad"  fed_scoll "Father some coll." fed_scollplus "Father some coll. + "  fed_collplus "Father coll+"  f_age "Father's age") 
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/all_summary.tex"'})

{com}. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-1: Summary statistics for restricted samples: 2002 and 2007
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab t_rest_prob t_rest_table2 t_rest_chcare t_rest_table4 using "$tables/all_restricted_summary.tex", booktabs  label  mtitle("High predicted probability of work for mothers" "Table 2 baseline" "Positive childcare expenditures"  "Table 4 baseline") cells( "count(fmt(%2.0f) label(N)) mean(fmt(%8.2f) label(Mean)) sd(fmt(%8.2f) label(SD))") ti(Summary statistics for restricted samples: 2002 and 2007) noobs  varwidth(15) modelwidth(11) wrap style(tex)  replace varlabels( ln_pratio_4ca "$\ln(\tilde{c -(}P{c )-}_{c -(}c,i{c )-})$"   year2007 "Year 2007"  ln_wage_f_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}f{c )-})$"  ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll." med_scollplus "Mother some coll. + "  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children" fed_hs "Father HS grad"  fed_scoll "Father some coll." fed_scollplus "Father some coll. + "  fed_collplus "Father coll+"  f_age "Father's age")
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/all_restricted_summary.tex"'})

{com}. 
. 
. 
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix  Figure G-6: Distributions of log relative input prices (PSID, 2002 and 2007)
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> * Distribution of relative prices
. 
. quietly kdensity ln_wage_m_rel 
{txt}
{com}. 
. local bdw_m: display %4.3f r(bwidth) 
{res}{txt}
{com}. 
. quietly kdensity ln_wage_f_rel 
{txt}
{com}. local bdw_f: display %4.3f r(bwidth) 
{res}{txt}
{com}. local krn=r(kernel) 
{txt}
{com}. 
. twoway (kdensity ln_wage_m_rel) (kdensity ln_wage_f_rel), legend(label(1 "Mother ") label(2 "Father")  region(lcolor(white))) ytitle("") /* title("Density plot: relative wages",  size(medlarge)) subtitle("( kernel: `krn'; bandwidth: mothers `bdw_m', fathers `bdw_f' )", size(medium)) */ xtitle("Relative wage") plotregion(fcolor(white)) bgcolor(white)  graphregion(fcolor(white) ilcolor(white) lcolor(white)) ylabel(, format(%03.2f) angle(horizontal))
{res}{txt}
{com}.         graph export "$graph/relative_wages.pdf", as(pdf) replace
{txt}(file D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\figures/relative_wages.pdf written in PDF format)

{com}. 
. 
. 
. quietly kdensity ln_pratio_4ca
{txt}
{com}. local bdw_c: display %4.3f r(bwidth) 
{res}{txt}
{com}. local krn_c=r(kernel) 
{txt}
{com}.         
. twoway (kdensity ln_pratio_4ca) , /* title("Density plot: relative childcare price",  size(medlarge)) subtitle("( kernel: `krn_c'; bandwidth: `bdw_c' )",  size(medium)) */  xtitle("") ytitle("") plotregion(fcolor(white)) bgcolor(white)  graphregion(fcolor(white) ilcolor(white) lcolor(white)) ylabel(, format(%03.2f) angle(horizontal))
{res}{txt}
{com}. graph export "$graph/relative_chcare.pdf", as(pdf) replace
{txt}(file D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\figures/relative_chcare.pdf written in PDF format)

{com}. 
.         
. 
. 
. if $est_time_goods==1 {c -(}
. 
. 
. 
. ****************************************************************************************************
. ******************** E. ESTIMATION: TIME RELATIVE TO HOUSEHOLD GOODS INPUTS (2002, 2007) ***********
. ****************************************************************************************************
. 
. clear 
. use "$temp/allfam_tmp.dta"
. 
. ************************** FULL SAMPLE (SINGLE MOTHERS AND TWO-PARENT HOUSEHOLDS)*************
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. 
. * Probability of work restriction > 0.75
. keep if prob_work_m >= `p_work_min_m' & prob_work_m !=.
{txt}(356 observations deleted)
{com}. 
. 
. *** Main estimates ***
. 
. * OLS estimates
. 
. forvalues zval=4/7 {c -(}
{txt}  2{com}.         disp _n(3) "OLS Specification " `zval' _n(2)
{txt}  3{com}.         regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm`zval''     
{txt}  4{com}. *       predict PHIm_ols`zval'
.         nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  5{com}.         capture noisily test `medvars'
{txt}  6{com}.         capture noisily test med_scoll med_collplus
{txt}  7{com}.         local rhom_ols`zval' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  8{com}.         estimates store estm_ols`zval'
{txt}  9{com}. {c )-}



OLS Specification 4



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       727
{txt}{hline 13}{c +}{hline 34}   F(10, 716)      = {res}    16.76
{txt}       Model {c |} {res} 193.573683        10  19.3573683   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 826.794496       716  1.15474092   {txt}R-squared       ={res}    0.1897
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1784
{txt}       Total {c |} {res} 1020.36818       726  1.40546581   {txt}Root MSE        =   {res} 1.0746

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6452337{col 27}{space 2}  .071377{col 38}{space 1}    9.04{col 47}{space 3}0.000{col 55}{space 4} .5051004{col 68}{space 3}  .785367
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0750842{col 27}{space 2} .0951348{col 38}{space 1}   -0.79{col 47}{space 3}0.430{col 55}{space 4}-.2618606{col 68}{space 3} .1116923
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1413157{col 27}{space 2} .0222791{col 38}{space 1}   -6.34{col 47}{space 3}0.000{col 55}{space 4}-.1850559{col 68}{space 3}-.0975755
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2} .0992183{col 27}{space 2} .3501145{col 38}{space 1}    0.28{col 47}{space 3}0.777{col 55}{space 4}-.5881555{col 68}{space 3}  .786592
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1061078{col 27}{space 2}    .3506{col 38}{space 1}    0.30{col 47}{space 3}0.762{col 55}{space 4}-.5822191{col 68}{space 3} .7944347
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} -.061088{col 27}{space 2} .3570282{col 38}{space 1}   -0.17{col 47}{space 3}0.864{col 55}{space 4}-.7620354{col 68}{space 3} .6398594
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0079797{col 27}{space 2} .0075507{col 38}{space 1}   -1.06{col 47}{space 3}0.291{col 55}{space 4}-.0228039{col 68}{space 3} .0068444
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2436214{col 27}{space 2} .0895959{col 38}{space 1}   -2.72{col 47}{space 3}0.007{col 55}{space 4}-.4195235{col 68}{space 3}-.0677192
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1564121{col 27}{space 2}  .125695{col 38}{space 1}    1.24{col 47}{space 3}0.214{col 55}{space 4}-.0903627{col 68}{space 3} .4031869
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0891213{col 27}{space 2} .0615566{col 38}{space 1}    1.45{col 47}{space 3}0.148{col 55}{space 4}-.0317317{col 68}{space 3} .2099742
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.126445{col 27}{space 2} .4687495{col 38}{space 1}    4.54{col 47}{space 3}0.000{col 55}{space 4} 1.206157{col 68}{space 3} 3.046733
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.818757{col 26}{space 2} .5671183{col 37}{space 1}   -3.21{col 46}{space 3}0.001{col 54}{space 4}-2.930288{col 67}{space 3}-.7072253
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}       F(  3,   716) ={res}    1.04
{txt}{col 13}Prob > F ={res}    0.3753

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   716) ={res}    1.33
{txt}{col 13}Prob > F ={res}    0.2644



OLS Specification 5



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       727
{txt}{hline 13}{c +}{hline 34}   F(9, 717)       = {res}    18.64
{txt}       Model {c |} {res} 193.480948         9  21.4978831   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 826.887232       717  1.15325974   {txt}R-squared       ={res}    0.1896
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1794
{txt}       Total {c |} {res} 1020.36818       726  1.40546581   {txt}Root MSE        =   {res} 1.0739

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6461114{col 27}{space 2}  .071264{col 38}{space 1}    9.07{col 47}{space 3}0.000{col 55}{space 4} .5062003{col 68}{space 3} .7860226
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0737061{col 27}{space 2} .0949495{col 38}{space 1}   -0.78{col 47}{space 3}0.438{col 55}{space 4}-.2601183{col 68}{space 3} .1127061
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1410849{col 27}{space 2} .0222499{col 38}{space 1}   -6.34{col 47}{space 3}0.000{col 55}{space 4}-.1847677{col 68}{space 3}-.0974021
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0110723{col 27}{space 2} .1021967{col 38}{space 1}    0.11{col 47}{space 3}0.914{col 55}{space 4}-.1895683{col 68}{space 3} .2117129
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1571411{col 27}{space 2} .1121116{col 38}{space 1}   -1.40{col 47}{space 3}0.161{col 55}{space 4}-.3772474{col 68}{space 3} .0629652
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} -.007913{col 27}{space 2} .0075422{col 38}{space 1}   -1.05{col 47}{space 3}0.294{col 55}{space 4}-.0227205{col 68}{space 3} .0068944
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2426265{col 27}{space 2} .0894697{col 38}{space 1}   -2.71{col 47}{space 3}0.007{col 55}{space 4}-.4182804{col 68}{space 3}-.0669726
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1582784{col 27}{space 2} .1254418{col 38}{space 1}    1.26{col 47}{space 3}0.207{col 55}{space 4}-.0879988{col 68}{space 3} .4045556
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}  .089088{col 27}{space 2}  .061517{col 38}{space 1}    1.45{col 47}{space 3}0.148{col 55}{space 4}-.0316869{col 68}{space 3} .2098629
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.213075{col 27}{space 2} .3551268{col 38}{space 1}    6.23{col 47}{space 3}0.000{col 55}{space 4} 1.515863{col 68}{space 3} 2.910288
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.825748{col 26}{space 2} .5690329{col 37}{space 1}   -3.21{col 46}{space 3}0.001{col 54}{space 4}-2.941032{col 67}{space 3}-.7104642
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   717) ={res}    1.52
{txt}{col 13}Prob > F ={res}    0.2198



OLS Specification 6



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       603
{txt}{hline 13}{c +}{hline 34}   F(10, 592)      = {res}    11.86
{txt}       Model {c |} {res} 136.728498        10  13.6728498   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 682.315472       592  1.15255992   {txt}R-squared       ={res}    0.1669
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1529
{txt}       Total {c |} {res}  819.04397       602  1.36053816   {txt}Root MSE        =   {res} 1.0736

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6087039{col 27}{space 2} .0784591{col 38}{space 1}    7.76{col 47}{space 3}0.000{col 55}{space 4} .4546119{col 68}{space 3} .7627958
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.1205546{col 27}{space 2} .1042018{col 38}{space 1}   -1.16{col 47}{space 3}0.248{col 55}{space 4}-.3252049{col 68}{space 3} .0840956
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1468768{col 27}{space 2} .0246937{col 38}{space 1}   -5.95{col 47}{space 3}0.000{col 55}{space 4}-.1953746{col 68}{space 3}-.0983789
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.0427821{col 27}{space 2} .1167906{col 38}{space 1}   -0.37{col 47}{space 3}0.714{col 55}{space 4}-.2721565{col 68}{space 3} .1865923
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2445759{col 27}{space 2} .1308821{col 38}{space 1}   -1.87{col 47}{space 3}0.062{col 55}{space 4}-.5016255{col 68}{space 3} .0124738
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0022151{col 27}{space 2} .0083137{col 38}{space 1}   -0.27{col 47}{space 3}0.790{col 55}{space 4} -.018543{col 68}{space 3} .0141128
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0954831{col 27}{space 2} .1069439{col 38}{space 1}   -0.89{col 47}{space 3}0.372{col 55}{space 4}-.3055187{col 68}{space 3} .1145524
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .0811334{col 27}{space 2} .1440093{col 38}{space 1}    0.56{col 47}{space 3}0.573{col 55}{space 4}-.2016979{col 68}{space 3} .3639647
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0903992{col 27}{space 2} .0677118{col 38}{space 1}    1.34{col 47}{space 3}0.182{col 55}{space 4}-.0425853{col 68}{space 3} .2233837
{txt}{space 7}m_pc97 {c |}{col 15}{res}{space 2} -.004812{col 27}{space 2} .0033039{col 38}{space 1}   -1.46{col 47}{space 3}0.146{col 55}{space 4}-.0113007{col 68}{space 3} .0016768
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  2.60172{col 27}{space 2} .4490435{col 38}{space 1}    5.79{col 47}{space 3}0.000{col 55}{space 4} 1.719808{col 68}{space 3} 3.483632
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.555609{col 26}{space 2}  .512427{col 37}{space 1}   -3.04{col 46}{space 3}0.002{col 54}{space 4}-2.559948{col 67}{space 3}-.5512709
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   592) ={res}    2.17
{txt}{col 13}Prob > F ={res}    0.1148



OLS Specification 7



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       562
{txt}{hline 13}{c +}{hline 34}   F(7, 554)       = {res}    18.98
{txt}       Model {c |} {res} 146.832692         7  20.9760989   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 612.114606       554  1.10490001   {txt}R-squared       ={res}    0.1935
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1833
{txt}       Total {c |} {res} 758.947299       561  1.35284723   {txt}Root MSE        =   {res} 1.0511

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7577128{col 27}{space 2} .0924308{col 38}{space 1}    8.20{col 47}{space 3}0.000{col 55}{space 4}  .576155{col 68}{space 3} .9392705
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2} .0223839{col 27}{space 2} .1082852{col 38}{space 1}    0.21{col 47}{space 3}0.836{col 55}{space 4} -.190316{col 68}{space 3} .2350837
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1470773{col 27}{space 2} .0240701{col 38}{space 1}   -6.11{col 47}{space 3}0.000{col 55}{space 4}-.1943571{col 68}{space 3}-.0997974
{txt}{space 2}ln_wageFE_m {c |}{col 15}{res}{space 2} -.345511{col 27}{space 2} .1141995{col 38}{space 1}   -3.03{col 47}{space 3}0.003{col 55}{space 4} -.569828{col 68}{space 3} -.121194
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.3279694{col 27}{space 2} .1019815{col 38}{space 1}   -3.22{col 47}{space 3}0.001{col 55}{space 4}-.5282871{col 68}{space 3}-.1276516
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1630933{col 27}{space 2} .1686452{col 38}{space 1}    0.97{col 47}{space 3}0.334{col 55}{space 4}-.1681689{col 68}{space 3} .4943555
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0266289{col 27}{space 2} .0664747{col 38}{space 1}    0.40{col 47}{space 3}0.689{col 55}{space 4}-.1039443{col 68}{space 3} .1572022
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.745042{col 27}{space 2} .3664893{col 38}{space 1}    4.76{col 47}{space 3}0.000{col 55}{space 4} 1.025164{col 68}{space 3} 2.464921
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-3.127333{col 26}{space 2} 1.574548{col 37}{space 1}   -1.99{col 46}{space 3}0.047{col 54}{space 4} -6.21339{col 67}{space 3}-.0412758
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found
{com}. 
.         
. * IV estimates: state of residence
. 
. local zval= 5 
.         disp _n(3) "IV Specification " `zval' _n(2)
{res}


IV Specification 5


{com}.         ivregress 2sls ln_tau_m_g_exp_ratio curr_married `Zm`zval''  (ln_wage_m_rel=i.state i.year)
{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       727
{txt}{col 1}{col 51}Wald chi2({res}9{txt}){col 67}= {res}     98.51
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1873
{txt}{col 51}Root MSE{col 67}=    {res}  1.068

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7489777{col 27}{space 2} .2162469{col 38}{space 1}    3.46{col 47}{space 3}0.001{col 55}{space 4} .3251416{col 68}{space 3} 1.172814
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0687963{col 27}{space 2} .0949332{col 38}{space 1}   -0.72{col 47}{space 3}0.469{col 55}{space 4} -.254862{col 68}{space 3} .1172695
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1392787{col 27}{space 2} .0224173{col 38}{space 1}   -6.21{col 47}{space 3}0.000{col 55}{space 4}-.1832159{col 68}{space 3}-.0953416
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.0178919{col 27}{space 2} .1167889{col 38}{space 1}   -0.15{col 47}{space 3}0.878{col 55}{space 4}-.2467938{col 68}{space 3} .2110101
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2177181{col 27}{space 2} .1640338{col 38}{space 1}   -1.33{col 47}{space 3}0.184{col 55}{space 4}-.5392184{col 68}{space 3} .1037822
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0090981{col 27}{space 2} .0078616{col 38}{space 1}   -1.16{col 47}{space 3}0.247{col 55}{space 4}-.0245066{col 68}{space 3} .0063104
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2493777{col 27}{space 2} .0899858{col 38}{space 1}   -2.77{col 47}{space 3}0.006{col 55}{space 4}-.4257467{col 68}{space 3}-.0730087
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1553326{col 27}{space 2} .1248941{col 38}{space 1}    1.24{col 47}{space 3}0.214{col 55}{space 4}-.0894553{col 68}{space 3} .4001205
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0973374{col 27}{space 2} .0633369{col 38}{space 1}    1.54{col 47}{space 3}0.124{col 55}{space 4}-.0268007{col 68}{space 3} .2214754
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.998622{col 27}{space 2} .5533124{col 38}{space 1}    3.61{col 47}{space 3}0.000{col 55}{space 4} .9141494{col 68}{space 3} 3.083094
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}curr_married age med_scoll med_collplus m_age m_white num_0_5 num_child 4.state 5.state 6.state 8.state 9.state 11.state 12.state 13.state 16.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 30.state 31.state 32.state 34.state 36.state 37.state 38.state 39.state 40.state 41.state 42.state 45.state 46.state 47.state 48.state 49.state 51.state 53.state 55.state 2007.year{p_end}
{com}. *       predict PHIm_ivs`zval'
.         nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.983709{col 26}{space 2} 3.431825{col 37}{space 1}   -0.87{col 46}{space 3}0.385{col 54}{space 4}-9.709963{col 67}{space 3} 3.742544
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}.         capture noisily test `medvars'
{bf}{err}med_hs{sf} not found
{com}.         capture noisily test med_scoll med_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    2.82
{txt}{col 10}Prob > chi2 =  {res}  0.2439
{com}.         local rhom_ivs`zval' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
.         estimates store estm_ivs`zval'
.         estat firststage
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(40,678){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.2764{col 29}{space 2}0.2252{col 41}{space 3}0.1074{col 53}{space 3}  2.0399{col 65}{space 3}0.0002
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}2.0399      

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   40
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}{com}.         estat endogenous

{txt}{col 3}Tests of endogeneity
{col 3}Ho: variables are exogenous

{col 3}Durbin (score) chi2({res}1{txt}){col 35}={res}{col 37} .254249{txt}  (p = {res}0.6141{txt})
{col 3}Wu-Hausman F({res}1{txt},{res}716{txt}){col 35}={res}{col 37}  .25049{txt}  (p = {res}0.6169{txt})
{com}.         local Hm`zval' = r(p)
. 
. 
. 
. * IV estimates: predicted wages
. 
. local zval= 5 
.         disp _n(3) "IV Specification " `zval' _n(2)
{res}


IV Specification 5


{com}.         ivregress 2sls ln_tau_m_g_exp_ratio curr_married `Zm`zval''  (ln_wage_m_rel=m_pw_inst)
{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       720
{txt}{col 1}{col 51}Wald chi2({res}9{txt}){col 67}= {res}     89.43
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1814
{txt}{col 51}Root MSE{col 67}=    {res} 1.0684

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .5529094{col 27}{space 2} .1964978{col 38}{space 1}    2.81{col 47}{space 3}0.005{col 55}{space 4} .1677807{col 68}{space 3}  .938038
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2} -.070662{col 27}{space 2} .0956019{col 38}{space 1}   -0.74{col 47}{space 3}0.460{col 55}{space 4}-.2580384{col 68}{space 3} .1167143
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1403226{col 27}{space 2} .0224348{col 38}{space 1}   -6.25{col 47}{space 3}0.000{col 55}{space 4} -.184294{col 68}{space 3}-.0963513
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0260108{col 27}{space 2} .1130247{col 38}{space 1}    0.23{col 47}{space 3}0.818{col 55}{space 4}-.1955136{col 68}{space 3} .2475352
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1192116{col 27}{space 2} .1545793{col 38}{space 1}   -0.77{col 47}{space 3}0.441{col 55}{space 4}-.4221814{col 68}{space 3} .1837582
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0071501{col 27}{space 2} .0077858{col 38}{space 1}   -0.92{col 47}{space 3}0.358{col 55}{space 4}  -.02241{col 68}{space 3} .0081097
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2327764{col 27}{space 2} .0905744{col 38}{space 1}   -2.57{col 47}{space 3}0.010{col 55}{space 4}-.4102989{col 68}{space 3}-.0552539
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1675752{col 27}{space 2} .1255892{col 38}{space 1}    1.33{col 47}{space 3}0.182{col 55}{space 4}-.0785751{col 68}{space 3} .4137254
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0823023{col 27}{space 2} .0628725{col 38}{space 1}    1.31{col 47}{space 3}0.191{col 55}{space 4}-.0409256{col 68}{space 3} .2055302
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.398073{col 27}{space 2} .5203368{col 38}{space 1}    4.61{col 47}{space 3}0.000{col 55}{space 4} 1.378232{col 68}{space 3} 3.417915
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}curr_married age med_scoll med_collplus m_age m_white num_0_5 num_child m_pw_inst{p_end}
{com}. *       predict PHIm_ivpw`zval'
.         nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.236683{col 26}{space 2} .9830297{col 37}{space 1}   -1.26{col 46}{space 3}0.208{col 54}{space 4}-3.163386{col 67}{space 3} .6900198
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}.         capture noisily test `medvars'
{bf}{err}med_hs{sf} not found
{com}.         capture noisily test med_scoll med_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    1.53
{txt}{col 10}Prob > chi2 =  {res}  0.4655
{com}.         local rhom_ivpw`zval' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
.         estimates store estm_ivpw`zval'
.         estat firststage
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,710){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.2985{col 29}{space 2}0.2896{col 41}{space 3}0.1358{col 53}{space 3} 111.602{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}111.602     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}{com}.         estat endogenous

{txt}{col 3}Tests of endogeneity
{col 3}Ho: variables are exogenous

{col 3}Durbin (score) chi2({res}1{txt}){col 35}={res}{col 37} .264327{txt}  (p = {res}0.6072{txt})
{col 3}Wu-Hausman F({res}1{txt},{res}709{txt}){col 35}={res}{col 37} .260384{txt}  (p = {res}0.6100{txt})
{com}.         local Hmpw`zval' = r(p)
. 
. 
. 
. ***  Checking various predicted wage measures *** 
. 
. eststo clear 
. local zval= 5 
. 
. 
.         ivreg2 ln_tau_m_g_exp_ratio curr_married `Zm`zval''  (ln_wage_m_rel=m_pred_lnwage_mean_occ_state), first savefirst savefprefix(iv_all_pr1_`zval')
{res}
{txt}Stored estimation results
{hline 25}
{hline 13}{c TT}{hline 70}
        name {c |} command      depvar       npar  title 
{hline 13}{c +}{hline 70}
{ralign 12:{stata estimates replay iv_all_pr1_5ln_wage_m_rel:iv_all_pr1~l}}{col 14}{txt:{c |}}{res}{col 16}{lalign 12:ivreg2}{col 29}{lalign 12:ln_wage_m_~l}{col 42}  10{col 48}{it:First-stage regression: ln_wage_m_rel}
{txt}{hline 13}{c BT}{hline 70}

First-stage regressions
{hline 23}


First-stage regression of ln_wage_m_rel:

Statistics consistent for homoskedasticity only
Number of obs = {col 31}{res}     720
{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               ln_wage_m_rel{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      t{col 62}   P>|t|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
m_pred_lnwage_mean_occ_state {c |}{col 30}{res}{space 2} 1.083517{col 42}{space 2} .1025651{col 53}{space 1}   10.56{col 62}{space 3}0.000{col 70}{space 4} .8821502{col 83}{space 3} 1.284885
{txt}{space 16}curr_married {c |}{col 30}{res}{space 2}-.0454612{col 42}{space 2} .0457896{col 53}{space 1}   -0.99{col 62}{space 3}0.321{col 70}{space 4}-.1353603{col 83}{space 3}  .044438
{txt}{space 25}age {c |}{col 30}{res}{space 2}-.0087502{col 42}{space 2} .0107388{col 53}{space 1}   -0.81{col 62}{space 3}0.415{col 70}{space 4}-.0298339{col 83}{space 3} .0123334
{txt}{space 19}med_scoll {c |}{col 30}{res}{space 2} .1905368{col 42}{space 2} .0487036{col 53}{space 1}    3.91{col 62}{space 3}0.000{col 70}{space 4} .0949166{col 83}{space 3} .2861571
{txt}{space 16}med_collplus {c |}{col 30}{res}{space 2} .4327726{col 42}{space 2} .0521595{col 53}{space 1}    8.30{col 62}{space 3}0.000{col 70}{space 4} .3303673{col 83}{space 3}  .535178
{txt}{space 23}m_age {c |}{col 30}{res}{space 2} .0086162{col 42}{space 2} .0036155{col 53}{space 1}    2.38{col 62}{space 3}0.017{col 70}{space 4} .0015178{col 83}{space 3} .0157145
{txt}{space 21}m_white {c |}{col 30}{res}{space 2} .0255628{col 42}{space 2} .0433079{col 53}{space 1}    0.59{col 62}{space 3}0.555{col 70}{space 4}-.0594641{col 83}{space 3} .1105898
{txt}{space 21}num_0_5 {c |}{col 30}{res}{space 2} .0389068{col 42}{space 2} .0604193{col 53}{space 1}    0.64{col 62}{space 3}0.520{col 70}{space 4} -.079715{col 83}{space 3} .1575287
{txt}{space 19}num_child {c |}{col 30}{res}{space 2}-.0474143{col 42}{space 2} .0295741{col 53}{space 1}   -1.60{col 62}{space 3}0.109{col 70}{space 4}-.1054774{col 83}{space 3} .0106488
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .2952533{col 42}{space 2} .2294195{col 53}{space 1}    1.29{col 62}{space 3}0.199{col 70}{space 4}-.1551685{col 83}{space 3}  .745675
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of excluded instruments:
  F(  1,   710) = {res}  111.60
{txt}  Prob > F      = {res}  0.0000
{help ivreg2##swstats:Sanderson-Windmeijer multivariate F test of excluded instruments:}
{txt}  F(  1,   710) = {res}  111.60
{txt}  Prob > F      = {res}  0.0000



{txt}Summary results for first-stage regressions
{hline 43}

{col 44}{help ivreg2##swstats:(Underid)}{col 65}{help ivreg2##swstats:(Weak id)}
Variable     |{col 16}{help ivreg2##swstats:F}({res}{col 17}  1{txt},{res}   710{txt})  P-val{col 37}|{col 39}{help ivreg2##swstats:SW Chi-sq}({res}  1{txt}) P-val{col 60}|{col 62}{help ivreg2##swstats:SW F}({res}{col 67}  1{txt},{res}   710{txt})
{res}ln_wage_m_re{col 14}{txt}|{col 18}{res}  111.60{col 28}  0.0000{col 37}{txt}|{col 42}{res}  113.17{col 51}  0.0000{col 60}{txt}|{col 65}{res}  111.60

{txt}Stock-Yogo weak ID F test critical values for single endogenous regressor:
{res}{txt}{col 36}10% maximal IV size{res}{col 67} 16.38
{txt}{col 36}15% maximal IV size{res}{col 67}  8.96
{txt}{col 36}20% maximal IV size{res}{col 67}  6.66
{txt}{col 36}25% maximal IV size{res}{col 67}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

{help ivreg2##idtest:Underidentification test}
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
{res}Anderson canon. corr. LM statistic{txt}{col 42}Chi-sq({res}1{txt})={res}97.80  {col 61}{txt}P-val={res}0.0000

{help ivreg2##widtest:Weak identification test}
{txt}Ho: equation is weakly identified
{res}Cragg-Donald Wald F statistic{col 65}  111.60

{txt}Stock-Yogo weak ID test critical values for K1=1 and L1=1:
{res}{txt}{col 36}10% maximal IV size{res}{col 67} 16.38
{txt}{col 36}15% maximal IV size{res}{col 67}  8.96
{txt}{col 36}20% maximal IV size{res}{col 67}  6.66
{txt}{col 36}25% maximal IV size{res}{col 67}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.

{help ivreg2##wirobust:Weak-instrument-robust inference}
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
{res}Anderson-Rubin Wald test{txt}{col 36}F({res}1{txt},{res}710{txt})={col 49}{res}   7.11{col 61}{txt}P-val={res}0.0078
Anderson-Rubin Wald test{txt}{col 36}Chi-sq({res}1{txt})={col 49}{res}   7.21{col 61}{txt}P-val={res}0.0072
Stock-Wright LM S statistic{txt}{col 36}Chi-sq({res}1{txt})={col 49}{res}   7.14{col 61}{txt}P-val={res}0.0075

{txt}Number of observations               N  = {res}       720
{txt}Number of regressors                 K  = {res}        10
{txt}Number of endogenous regressors      K1 = {res}         1
{txt}Number of instruments                L  = {res}        10
{txt}Number of excluded instruments       L1 = {res}         1

{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

{col 55}Number of obs = {res}     720
{txt}{col 55}F(  9,   710) = {res}    9.80
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1003.932543{txt}{col 55}Centered R2   = {res}  0.1814
{txt}Total (uncentered) SS   = {res} 4278.266043{txt}{col 55}Uncentered R2 = {res}  0.8079
{txt}Residual SS             = {res} 821.7949647{txt}{col 55}Root MSE      = {res}   1.068

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .5529094{col 27}{space 2} .1964978{col 38}{space 1}    2.81{col 47}{space 3}0.005{col 55}{space 4} .1677807{col 68}{space 3}  .938038
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2} -.070662{col 27}{space 2} .0956019{col 38}{space 1}   -0.74{col 47}{space 3}0.460{col 55}{space 4}-.2580384{col 68}{space 3} .1167143
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1403226{col 27}{space 2} .0224348{col 38}{space 1}   -6.25{col 47}{space 3}0.000{col 55}{space 4} -.184294{col 68}{space 3}-.0963513
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0260108{col 27}{space 2} .1130247{col 38}{space 1}    0.23{col 47}{space 3}0.818{col 55}{space 4}-.1955136{col 68}{space 3} .2475352
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1192116{col 27}{space 2} .1545793{col 38}{space 1}   -0.77{col 47}{space 3}0.441{col 55}{space 4}-.4221814{col 68}{space 3} .1837582
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0071501{col 27}{space 2} .0077858{col 38}{space 1}   -0.92{col 47}{space 3}0.358{col 55}{space 4}  -.02241{col 68}{space 3} .0081097
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2327764{col 27}{space 2} .0905744{col 38}{space 1}   -2.57{col 47}{space 3}0.010{col 55}{space 4}-.4102989{col 68}{space 3}-.0552539
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1675752{col 27}{space 2} .1255892{col 38}{space 1}    1.33{col 47}{space 3}0.182{col 55}{space 4}-.0785751{col 68}{space 3} .4137254
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0823023{col 27}{space 2} .0628725{col 38}{space 1}    1.31{col 47}{space 3}0.191{col 55}{space 4}-.0409256{col 68}{space 3} .2055302
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.398073{col 27}{space 2} .5203368{col 38}{space 1}    4.61{col 47}{space 3}0.000{col 55}{space 4} 1.378232{col 68}{space 3} 3.417915
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Anderson canon. corr. LM statistic):{res}{col 71}  97.801
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0000
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71} 111.602
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
{hline 78}
{help ivreg2##overidtests:Sargan statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}ln_wage_m_rel
Included instruments:{col 23}curr_married age med_scoll med_collplus m_age m_white
{col 23}num_0_5 num_child
Excluded instruments:{col 23}m_pred_lnwage_mean_occ_state
{hline 78}
{com}.         estimates store est_all_iv_pr1_`zval'
.         estadd scalar F_all_`zval' = `e(widstat)' : iv_all_pr1_`zval'*
. 
.         ivreg2 ln_tau_m_g_exp_ratio curr_married `Zm`zval''  (ln_wage_m_rel=m_pred_lnwage_mean_occ),  first savefirst savefprefix(iv_all_pr2_`zval')
{res}
{txt}Stored estimation results
{hline 25}
{hline 13}{c TT}{hline 70}
        name {c |} command      depvar       npar  title 
{hline 13}{c +}{hline 70}
{ralign 12:{stata estimates replay iv_all_pr2_5ln_wage_m_rel:iv_all_pr2~l}}{col 14}{txt:{c |}}{res}{col 16}{lalign 12:ivreg2}{col 29}{lalign 12:ln_wage_m_~l}{col 42}  10{col 48}{it:First-stage regression: ln_wage_m_rel}
{txt}{hline 13}{c BT}{hline 70}

First-stage regressions
{hline 23}


First-stage regression of ln_wage_m_rel:

Statistics consistent for homoskedasticity only
Number of obs = {col 31}{res}     720
{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         ln_wage_m_rel{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      t{col 56}   P>|t|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
m_pred_lnwage_mean_occ {c |}{col 24}{res}{space 2} 1.001793{col 36}{space 2} .1100767{col 47}{space 1}    9.10{col 56}{space 3}0.000{col 64}{space 4} .7856782{col 77}{space 3} 1.217908
{txt}{space 10}curr_married {c |}{col 24}{res}{space 2}-.0569226{col 36}{space 2} .0466105{col 47}{space 1}   -1.22{col 56}{space 3}0.222{col 64}{space 4}-.1484334{col 77}{space 3} .0345883
{txt}{space 19}age {c |}{col 24}{res}{space 2}-.0110115{col 36}{space 2} .0109254{col 47}{space 1}   -1.01{col 56}{space 3}0.314{col 64}{space 4}-.0324615{col 77}{space 3} .0104384
{txt}{space 13}med_scoll {c |}{col 24}{res}{space 2}  .200575{col 36}{space 2} .0495727{col 47}{space 1}    4.05{col 56}{space 3}0.000{col 64}{space 4} .1032484{col 77}{space 3} .2979017
{txt}{space 10}med_collplus {c |}{col 24}{res}{space 2}  .448102{col 36}{space 2} .0531827{col 47}{space 1}    8.43{col 56}{space 3}0.000{col 64}{space 4} .3436878{col 77}{space 3} .5525161
{txt}{space 17}m_age {c |}{col 24}{res}{space 2} .0121937{col 36}{space 2} .0036781{col 47}{space 1}    3.32{col 56}{space 3}0.001{col 64}{space 4} .0049724{col 77}{space 3}  .019415
{txt}{space 15}m_white {c |}{col 24}{res}{space 2} .0894719{col 36}{space 2} .0438662{col 47}{space 1}    2.04{col 56}{space 3}0.042{col 64}{space 4}  .003349{col 77}{space 3} .1755948
{txt}{space 15}num_0_5 {c |}{col 24}{res}{space 2} .0317096{col 36}{space 2} .0615041{col 47}{space 1}    0.52{col 56}{space 3}0.606{col 64}{space 4}-.0890422{col 77}{space 3} .1524613
{txt}{space 13}num_child {c |}{col 24}{res}{space 2}-.0554187{col 36}{space 2} .0300662{col 47}{space 1}   -1.84{col 56}{space 3}0.066{col 64}{space 4} -.114448{col 77}{space 3} .0036107
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4280736{col 36}{space 2}  .240583{col 47}{space 1}    1.78{col 56}{space 3}0.076{col 64}{space 4}-.0442656{col 77}{space 3} .9004129
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of excluded instruments:
  F(  1,   710) = {res}   82.83
{txt}  Prob > F      = {res}  0.0000
{help ivreg2##swstats:Sanderson-Windmeijer multivariate F test of excluded instruments:}
{txt}  F(  1,   710) = {res}   82.83
{txt}  Prob > F      = {res}  0.0000



{txt}Summary results for first-stage regressions
{hline 43}

{col 44}{help ivreg2##swstats:(Underid)}{col 65}{help ivreg2##swstats:(Weak id)}
Variable     |{col 16}{help ivreg2##swstats:F}({res}{col 17}  1{txt},{res}   710{txt})  P-val{col 37}|{col 39}{help ivreg2##swstats:SW Chi-sq}({res}  1{txt}) P-val{col 60}|{col 62}{help ivreg2##swstats:SW F}({res}{col 67}  1{txt},{res}   710{txt})
{res}ln_wage_m_re{col 14}{txt}|{col 18}{res}   82.83{col 28}  0.0000{col 37}{txt}|{col 42}{res}   83.99{col 51}  0.0000{col 60}{txt}|{col 65}{res}   82.83

{txt}Stock-Yogo weak ID F test critical values for single endogenous regressor:
{res}{txt}{col 36}10% maximal IV size{res}{col 67} 16.38
{txt}{col 36}15% maximal IV size{res}{col 67}  8.96
{txt}{col 36}20% maximal IV size{res}{col 67}  6.66
{txt}{col 36}25% maximal IV size{res}{col 67}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

{help ivreg2##idtest:Underidentification test}
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
{res}Anderson canon. corr. LM statistic{txt}{col 42}Chi-sq({res}1{txt})={res}75.22  {col 61}{txt}P-val={res}0.0000

{help ivreg2##widtest:Weak identification test}
{txt}Ho: equation is weakly identified
{res}Cragg-Donald Wald F statistic{col 65}   82.83

{txt}Stock-Yogo weak ID test critical values for K1=1 and L1=1:
{res}{txt}{col 36}10% maximal IV size{res}{col 67} 16.38
{txt}{col 36}15% maximal IV size{res}{col 67}  8.96
{txt}{col 36}20% maximal IV size{res}{col 67}  6.66
{txt}{col 36}25% maximal IV size{res}{col 67}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.

{help ivreg2##wirobust:Weak-instrument-robust inference}
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
{res}Anderson-Rubin Wald test{txt}{col 36}F({res}1{txt},{res}710{txt})={col 49}{res}   2.30{col 61}{txt}P-val={res}0.1302
Anderson-Rubin Wald test{txt}{col 36}Chi-sq({res}1{txt})={col 49}{res}   2.33{col 61}{txt}P-val={res}0.1270
Stock-Wright LM S statistic{txt}{col 36}Chi-sq({res}1{txt})={col 49}{res}   2.32{col 61}{txt}P-val={res}0.1277

{txt}Number of observations               N  = {res}       720
{txt}Number of regressors                 K  = {res}        10
{txt}Number of endogenous regressors      K1 = {res}         1
{txt}Number of instruments                L  = {res}        10
{txt}Number of excluded instruments       L1 = {res}         1

{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

{col 55}Number of obs = {res}     720
{txt}{col 55}F(  9,   710) = {res}    9.04
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1003.932543{txt}{col 55}Centered R2   = {res}  0.1654
{txt}Total (uncentered) SS   = {res} 4278.266043{txt}{col 55}Uncentered R2 = {res}  0.8042
{txt}Residual SS             = {res} 837.8486691{txt}{col 55}Root MSE      = {res}   1.079

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .3593674{col 27}{space 2} .2262406{col 38}{space 1}    1.59{col 47}{space 3}0.112{col 55}{space 4} -.084056{col 68}{space 3} .8027908
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0808436{col 27}{space 2} .0967005{col 38}{space 1}   -0.84{col 47}{space 3}0.403{col 55}{space 4} -.270373{col 68}{space 3} .1086859
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1429977{col 27}{space 2} .0227026{col 38}{space 1}   -6.30{col 47}{space 3}0.000{col 55}{space 4}-.1874941{col 68}{space 3}-.0985014
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0780336{col 27}{space 2} .1178052{col 38}{space 1}    0.66{col 47}{space 3}0.508{col 55}{space 4}-.1528604{col 68}{space 3} .3089276
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.0068451{col 27}{space 2}  .168361{col 38}{space 1}   -0.04{col 47}{space 3}0.968{col 55}{space 4}-.3368267{col 68}{space 3} .3231365
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0050684{col 27}{space 2} .0079479{col 38}{space 1}   -0.64{col 47}{space 3}0.524{col 55}{space 4} -.020646{col 68}{space 3} .0105093
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2183757{col 27}{space 2} .0918118{col 38}{space 1}   -2.38{col 47}{space 3}0.017{col 55}{space 4}-.3983236{col 68}{space 3}-.0384279
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1740629{col 27}{space 2} .1268623{col 38}{space 1}    1.37{col 47}{space 3}0.170{col 55}{space 4}-.0745826{col 68}{space 3} .4227084
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0679832{col 27}{space 2} .0639912{col 38}{space 1}    1.06{col 47}{space 3}0.288{col 55}{space 4}-.0574371{col 68}{space 3} .1934036
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.799698{col 27}{space 2} .5717819{col 38}{space 1}    4.90{col 47}{space 3}0.000{col 55}{space 4} 1.679026{col 68}{space 3}  3.92037
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Anderson canon. corr. LM statistic):{res}{col 71}  75.218
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0000
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  82.826
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
{hline 78}
{help ivreg2##overidtests:Sargan statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}ln_wage_m_rel
Included instruments:{col 23}curr_married age med_scoll med_collplus m_age m_white
{col 23}num_0_5 num_child
Excluded instruments:{col 23}m_pred_lnwage_mean_occ
{hline 78}
{com}.         estimates store est_all_iv_pr2_`zval'
.         estadd scalar F_all_`zval' = `e(widstat)' : iv_all_pr2_`zval'*
. 
.         
.         ivreg2 ln_tau_m_g_exp_ratio curr_married `Zm`zval''  (ln_wage_m_rel=m_pred_lnwage_mean_state),  first savefirst savefprefix(iv_all_pr3_`zval')
{res}
{txt}Stored estimation results
{hline 25}
{hline 13}{c TT}{hline 70}
        name {c |} command      depvar       npar  title 
{hline 13}{c +}{hline 70}
{ralign 12:{stata estimates replay iv_all_pr3_5ln_wage_m_rel:iv_all_pr3~l}}{col 14}{txt:{c |}}{res}{col 16}{lalign 12:ivreg2}{col 29}{lalign 12:ln_wage_m_~l}{col 42}  10{col 48}{it:First-stage regression: ln_wage_m_rel}
{txt}{hline 13}{c BT}{hline 70}

First-stage regressions
{hline 23}


First-stage regression of ln_wage_m_rel:

Statistics consistent for homoskedasticity only
Number of obs = {col 31}{res}     720
{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           ln_wage_m_rel{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
m_pred_lnwage_mean_state {c |}{col 26}{res}{space 2}  .762211{col 38}{space 2} .1778722{col 49}{space 1}    4.29{col 58}{space 3}0.000{col 66}{space 4} .4129926{col 79}{space 3} 1.111429
{txt}{space 12}curr_married {c |}{col 26}{res}{space 2}-.0550488{col 38}{space 2} .0486302{col 49}{space 1}   -1.13{col 58}{space 3}0.258{col 66}{space 4}-.1505249{col 79}{space 3} .0404274
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0117096{col 38}{space 2} .0114047{col 49}{space 1}   -1.03{col 58}{space 3}0.305{col 66}{space 4}-.0341006{col 79}{space 3} .0106815
{txt}{space 15}med_scoll {c |}{col 26}{res}{space 2} .2700998{col 38}{space 2} .0511261{col 49}{space 1}    5.28{col 58}{space 3}0.000{col 66}{space 4} .1697233{col 79}{space 3} .3704763
{txt}{space 12}med_collplus {c |}{col 26}{res}{space 2} .6097848{col 38}{space 2} .0538008{col 49}{space 1}   11.33{col 58}{space 3}0.000{col 66}{space 4} .5041572{col 79}{space 3} .7154124
{txt}{space 19}m_age {c |}{col 26}{res}{space 2} .0085701{col 38}{space 2} .0038677{col 49}{space 1}    2.22{col 58}{space 3}0.027{col 66}{space 4} .0009766{col 79}{space 3} .0161636
{txt}{space 17}m_white {c |}{col 26}{res}{space 2} .0728622{col 38}{space 2}  .045735{col 49}{space 1}    1.59{col 58}{space 3}0.112{col 66}{space 4}-.0169298{col 79}{space 3} .1626543
{txt}{space 17}num_0_5 {c |}{col 26}{res}{space 2} .0415598{col 38}{space 2} .0641952{col 49}{space 1}    0.65{col 58}{space 3}0.518{col 66}{space 4}-.0844754{col 79}{space 3} .1675949
{txt}{space 15}num_child {c |}{col 26}{res}{space 2}-.0686511{col 38}{space 2}  .031321{col 49}{space 1}   -2.19{col 58}{space 3}0.029{col 66}{space 4}-.1301439{col 79}{space 3}-.0071583
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .5374053{col 38}{space 2} .3951244{col 49}{space 1}    1.36{col 58}{space 3}0.174{col 66}{space 4}-.2383467{col 79}{space 3} 1.313157
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of excluded instruments:
  F(  1,   710) = {res}   18.36
{txt}  Prob > F      = {res}  0.0000
{help ivreg2##swstats:Sanderson-Windmeijer multivariate F test of excluded instruments:}
{txt}  F(  1,   710) = {res}   18.36
{txt}  Prob > F      = {res}  0.0000



{txt}Summary results for first-stage regressions
{hline 43}

{col 44}{help ivreg2##swstats:(Underid)}{col 65}{help ivreg2##swstats:(Weak id)}
Variable     |{col 16}{help ivreg2##swstats:F}({res}{col 17}  1{txt},{res}   710{txt})  P-val{col 37}|{col 39}{help ivreg2##swstats:SW Chi-sq}({res}  1{txt}) P-val{col 60}|{col 62}{help ivreg2##swstats:SW F}({res}{col 67}  1{txt},{res}   710{txt})
{res}ln_wage_m_re{col 14}{txt}|{col 18}{res}   18.36{col 28}  0.0000{col 37}{txt}|{col 42}{res}   18.62{col 51}  0.0000{col 60}{txt}|{col 65}{res}   18.36

{txt}Stock-Yogo weak ID F test critical values for single endogenous regressor:
{res}{txt}{col 36}10% maximal IV size{res}{col 67} 16.38
{txt}{col 36}15% maximal IV size{res}{col 67}  8.96
{txt}{col 36}20% maximal IV size{res}{col 67}  6.66
{txt}{col 36}25% maximal IV size{res}{col 67}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

{help ivreg2##idtest:Underidentification test}
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
{res}Anderson canon. corr. LM statistic{txt}{col 42}Chi-sq({res}1{txt})={res}18.15  {col 61}{txt}P-val={res}0.0000

{help ivreg2##widtest:Weak identification test}
{txt}Ho: equation is weakly identified
{res}Cragg-Donald Wald F statistic{col 65}   18.36

{txt}Stock-Yogo weak ID test critical values for K1=1 and L1=1:
{res}{txt}{col 36}10% maximal IV size{res}{col 67} 16.38
{txt}{col 36}15% maximal IV size{res}{col 67}  8.96
{txt}{col 36}20% maximal IV size{res}{col 67}  6.66
{txt}{col 36}25% maximal IV size{res}{col 67}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.

{help ivreg2##wirobust:Weak-instrument-robust inference}
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
{res}Anderson-Rubin Wald test{txt}{col 36}F({res}1{txt},{res}710{txt})={col 49}{res}   2.74{col 61}{txt}P-val={res}0.0981
Anderson-Rubin Wald test{txt}{col 36}Chi-sq({res}1{txt})={col 49}{res}   2.78{col 61}{txt}P-val={res}0.0953
Stock-Wright LM S statistic{txt}{col 36}Chi-sq({res}1{txt})={col 49}{res}   2.77{col 61}{txt}P-val={res}0.0960

{txt}Number of observations               N  = {res}       720
{txt}Number of regressors                 K  = {res}        10
{txt}Number of endogenous regressors      K1 = {res}         1
{txt}Number of instruments                L  = {res}        10
{txt}Number of excluded instruments       L1 = {res}         1

{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

{col 55}Number of obs = {res}     720
{txt}{col 55}F(  9,   710) = {res}    9.23
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1003.932543{txt}{col 55}Centered R2   = {res}  0.1783
{txt}Total (uncentered) SS   = {res} 4278.266043{txt}{col 55}Uncentered R2 = {res}  0.8072
{txt}Residual SS             = {res} 824.9550571{txt}{col 55}Root MSE      = {res}    1.07

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7994223{col 27}{space 2} .4569863{col 38}{space 1}    1.75{col 47}{space 3}0.080{col 55}{space 4}-.0962544{col 68}{space 3} 1.695099
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0576939{col 27}{space 2} .0982118{col 38}{space 1}   -0.59{col 47}{space 3}0.557{col 55}{space 4}-.2501854{col 68}{space 3} .1347976
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1369153{col 27}{space 2} .0231894{col 38}{space 1}   -5.90{col 47}{space 3}0.000{col 55}{space 4}-.1823657{col 68}{space 3} -.091465
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.0402503{col 27}{space 2} .1584668{col 38}{space 1}   -0.25{col 47}{space 3}0.799{col 55}{space 4}-.3508395{col 68}{space 3}  .270339
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} -.262332{col 27}{space 2} .2851575{col 38}{space 1}   -0.92{col 47}{space 3}0.358{col 55}{space 4}-.8212305{col 68}{space 3} .2965665
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0098017{col 27}{space 2} .0089738{col 38}{space 1}   -1.09{col 47}{space 3}0.275{col 55}{space 4}  -.02739{col 68}{space 3} .0077866
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2511184{col 27}{space 2} .0957959{col 38}{space 1}   -2.62{col 47}{space 3}0.009{col 55}{space 4}-.4388748{col 68}{space 3} -.063362
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1593119{col 27}{space 2} .1265875{col 38}{space 1}    1.26{col 47}{space 3}0.208{col 55}{space 4}-.0887951{col 68}{space 3} .4074188
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1005404{col 27}{space 2} .0699936{col 38}{space 1}    1.44{col 47}{space 3}0.151{col 55}{space 4}-.0366445{col 68}{space 3} .2377252
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.886527{col 27}{space 2} 1.002082{col 38}{space 1}    1.88{col 47}{space 3}0.060{col 55}{space 4}-.0775178{col 68}{space 3} 3.850572
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Anderson canon. corr. LM statistic):{res}{col 71}  18.152
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0000
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  18.363
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
{hline 78}
{help ivreg2##overidtests:Sargan statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}ln_wage_m_rel
Included instruments:{col 23}curr_married age med_scoll med_collplus m_age m_white
{col 23}num_0_5 num_child
Excluded instruments:{col 23}m_pred_lnwage_mean_state
{hline 78}
{com}.         estimates store est_all_iv_pr3_`zval'
.         estadd scalar F_all_`zval' = `e(widstat)' : iv_all_pr3_`zval'*
. 
.         
.         ivreg2 ln_tau_m_g_exp_ratio curr_married `Zm`zval''  (ln_wage_m_rel=m_pred_lnwage), first savefirst savefprefix(iv_all_pr4_`zval')
{res}
{txt}Stored estimation results
{hline 25}
{hline 13}{c TT}{hline 70}
        name {c |} command      depvar       npar  title 
{hline 13}{c +}{hline 70}
{ralign 12:{stata estimates replay iv_all_pr4_5ln_wage_m_rel:iv_all_pr4~l}}{col 14}{txt:{c |}}{res}{col 16}{lalign 12:ivreg2}{col 29}{lalign 12:ln_wage_m_~l}{col 42}  10{col 48}{it:First-stage regression: ln_wage_m_rel}
{txt}{hline 13}{c BT}{hline 70}

First-stage regressions
{hline 23}


First-stage regression of ln_wage_m_rel:

Statistics consistent for homoskedasticity only
Number of obs = {col 31}{res}     720
{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_wage_m_rel{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
m_pred_lnwage {c |}{col 15}{res}{space 2} .3726035{col 27}{space 2} .2087142{col 38}{space 1}    1.79{col 47}{space 3}0.075{col 55}{space 4}-.0371674{col 68}{space 3} .7823744
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0578627{col 27}{space 2} .0492296{col 38}{space 1}   -1.18{col 47}{space 3}0.240{col 55}{space 4}-.1545157{col 68}{space 3} .0387903
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0134881{col 27}{space 2} .0115162{col 38}{space 1}   -1.17{col 47}{space 3}0.242{col 55}{space 4}-.0360979{col 68}{space 3} .0091217
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .2709703{col 27}{space 2} .0516807{col 38}{space 1}    5.24{col 47}{space 3}0.000{col 55}{space 4}  .169505{col 68}{space 3} .3724355
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .5964115{col 27}{space 2} .0546564{col 38}{space 1}   10.91{col 47}{space 3}0.000{col 55}{space 4}  .489104{col 68}{space 3}  .703719
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} .0109581{col 27}{space 2} .0038761{col 38}{space 1}    2.83{col 47}{space 3}0.005{col 55}{space 4} .0033481{col 68}{space 3} .0185682
{txt}{space 6}m_white {c |}{col 15}{res}{space 2} .0960512{col 27}{space 2} .0477815{col 38}{space 1}    2.01{col 47}{space 3}0.045{col 55}{space 4} .0022412{col 68}{space 3} .1898611
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .0349249{col 27}{space 2} .0648517{col 38}{space 1}    0.54{col 47}{space 3}0.590{col 55}{space 4}-.0923991{col 68}{space 3} .1622488
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}-.0736089{col 27}{space 2} .0316282{col 38}{space 1}   -2.33{col 47}{space 3}0.020{col 55}{space 4}-.1357048{col 68}{space 3} -.011513
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.322889{col 27}{space 2} .4533035{col 38}{space 1}    2.92{col 47}{space 3}0.004{col 55}{space 4} .4329132{col 68}{space 3} 2.212865
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of excluded instruments:
  F(  1,   710) = {res}    3.19
{txt}  Prob > F      = {res}  0.0747
{help ivreg2##swstats:Sanderson-Windmeijer multivariate F test of excluded instruments:}
{txt}  F(  1,   710) = {res}    3.19
{txt}  Prob > F      = {res}  0.0747



{txt}Summary results for first-stage regressions
{hline 43}

{col 44}{help ivreg2##swstats:(Underid)}{col 65}{help ivreg2##swstats:(Weak id)}
Variable     |{col 16}{help ivreg2##swstats:F}({res}{col 17}  1{txt},{res}   710{txt})  P-val{col 37}|{col 39}{help ivreg2##swstats:SW Chi-sq}({res}  1{txt}) P-val{col 60}|{col 62}{help ivreg2##swstats:SW F}({res}{col 67}  1{txt},{res}   710{txt})
{res}ln_wage_m_re{col 14}{txt}|{col 18}{res}    3.19{col 28}  0.0747{col 37}{txt}|{col 42}{res}    3.23{col 51}  0.0722{col 60}{txt}|{col 65}{res}    3.19

{txt}Stock-Yogo weak ID F test critical values for single endogenous regressor:
{res}{txt}{col 36}10% maximal IV size{res}{col 67} 16.38
{txt}{col 36}15% maximal IV size{res}{col 67}  8.96
{txt}{col 36}20% maximal IV size{res}{col 67}  6.66
{txt}{col 36}25% maximal IV size{res}{col 67}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

{help ivreg2##idtest:Underidentification test}
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
{res}Anderson canon. corr. LM statistic{txt}{col 42}Chi-sq({res}1{txt})={res}3.22   {col 61}{txt}P-val={res}0.0729

{help ivreg2##widtest:Weak identification test}
{txt}Ho: equation is weakly identified
{res}Cragg-Donald Wald F statistic{col 65}    3.19

{txt}Stock-Yogo weak ID test critical values for K1=1 and L1=1:
{res}{txt}{col 36}10% maximal IV size{res}{col 67} 16.38
{txt}{col 36}15% maximal IV size{res}{col 67}  8.96
{txt}{col 36}20% maximal IV size{res}{col 67}  6.66
{txt}{col 36}25% maximal IV size{res}{col 67}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.

{help ivreg2##wirobust:Weak-instrument-robust inference}
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
{res}Anderson-Rubin Wald test{txt}{col 36}F({res}1{txt},{res}710{txt})={col 49}{res}   0.15{col 61}{txt}P-val={res}0.6952
Anderson-Rubin Wald test{txt}{col 36}Chi-sq({res}1{txt})={col 49}{res}   0.16{col 61}{txt}P-val={res}0.6931
Stock-Wright LM S statistic{txt}{col 36}Chi-sq({res}1{txt})={col 49}{res}   0.16{col 61}{txt}P-val={res}0.6931

{txt}Number of observations               N  = {res}       720
{txt}Number of regressors                 K  = {res}        10
{txt}Number of endogenous regressors      K1 = {res}         1
{txt}Number of instruments                L  = {res}        10
{txt}Number of excluded instruments       L1 = {res}         1

{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

{col 55}Number of obs = {res}     720
{txt}{col 55}F(  9,   710) = {res}    6.80
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1003.932543{txt}{col 55}Centered R2   = {res} -0.0774
{txt}Total (uncentered) SS   = {res} 4278.266043{txt}{col 55}Uncentered R2 = {res}  0.7472
{txt}Residual SS             = {res} 1081.677267{txt}{col 55}Root MSE      = {res}   1.226

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2}-.4500937{col 27}{space 2} 1.242903{col 38}{space 1}   -0.36{col 47}{space 3}0.717{col 55}{space 4}-2.886138{col 68}{space 3} 1.985951
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.1234262{col 27}{space 2} .1271398{col 38}{space 1}   -0.97{col 47}{space 3}0.332{col 55}{space 4}-.3726157{col 68}{space 3} .1257632
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1541861{col 27}{space 2} .0307881{col 38}{space 1}   -5.01{col 47}{space 3}0.000{col 55}{space 4}-.2145297{col 68}{space 3}-.0938425
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .2956114{col 27}{space 2} .3532063{col 38}{space 1}    0.84{col 47}{space 3}0.403{col 55}{space 4}-.3966602{col 68}{space 3}  .987883
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .4631115{col 27}{space 2} .7314594{col 38}{space 1}    0.63{col 47}{space 3}0.527{col 55}{space 4}-.9705225{col 68}{space 3} 1.896746
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} .0036384{col 27}{space 2} .0158946{col 38}{space 1}    0.23{col 47}{space 3}0.819{col 55}{space 4}-.0275144{col 68}{space 3} .0347911
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1581471{col 27}{space 2} .1380909{col 38}{space 1}   -1.15{col 47}{space 3}0.252{col 55}{space 4}-.4288002{col 68}{space 3} .1125061
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2011968{col 27}{space 2} .1497974{col 38}{space 1}    1.34{col 47}{space 3}0.179{col 55}{space 4}-.0924007{col 68}{space 3} .4947943
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0080958{col 27}{space 2} .1156746{col 38}{space 1}    0.07{col 47}{space 3}0.944{col 55}{space 4}-.2186222{col 68}{space 3} .2348139
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 4.479434{col 27}{space 2} 2.605708{col 38}{space 1}    1.72{col 47}{space 3}0.086{col 55}{space 4}-.6276586{col 68}{space 3} 9.586527
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Anderson canon. corr. LM statistic):{res}{col 71}   3.218
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0729
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   3.187
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
{hline 78}
{help ivreg2##overidtests:Sargan statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}ln_wage_m_rel
Included instruments:{col 23}curr_married age med_scoll med_collplus m_age m_white
{col 23}num_0_5 num_child
Excluded instruments:{col 23}m_pred_lnwage
{hline 78}
{com}.         estimates store est_all_iv_pr4_`zval'
.         estadd scalar F_all_`zval' = `e(widstat)' : iv_all_pr4_`zval'*
. 
. 
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Table 2: OLS and 2SLS estimates for mother time/goods relative demand
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab  estm_ols4 estm_ols5 estm_ols6 estm_ols7  estm_ivpw5  estm_ivs5 using "$tables/All_S1m.tex", coeflab(ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m,i{c )-})$" curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children"  ln_wageFE_m "Mother's log wage FE" m_pc97 "Mother's cogn. score") stats(r2 N, fmt(3 0))  ti(OLS estimates for mother time/goods relative demand (ALL, 1-2 kids ages 0-12, P(work) $\geq$`p_work_min_m', 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(15) modelwidth(11) wrap style(tex) mtitles("OLS" "OLS" "OLS" "OLS" "IV (pred wage)" "IV (state, year)" )  replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/All_S1m.tex"'})
{com}. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-5: 2SLS estimates for mother time/goods relative demand using
. *** different predicted wage measures as instruments
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. *** IV estimation with different predicted wage measures as instruments
. 
. esttab  est_all_iv_pr1_5 est_all_iv_pr2_5 est_all_iv_pr3_5 est_all_iv_pr4_5 using "$tables/All_S1m_IV_pred_5.tex",  coeflab(  ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m,i{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children") stats(N, fmt(0))  ti(Appendix Table: IV estimates for mother time/goods relative demand using different predicted wage measures as instruments (ALL, 1-2 kids ages 0-12, P(work) $\geq$ `p_work_min_m', 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(15) modelwidth(10) wrap style(tex)  /*
> */ mtitles("Uses All Variation" "Excludes Avg. State Diff." "Excludes Avg. Occ Diff." "Excludes Avg. State and Occ Diff." )  replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/All_S1m_IV_pred_5.tex"'})
{com}. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-4: First-stage estimates for mother time/goods relative demand using 
. *** different predicted wage measures as instruments
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. *First-stage of IV estimation with different predicted wage measures as instruments
. 
. esttab  iv_all_pr1_5* iv_all_pr2_5* iv_all_pr3_5* iv_all_pr4_5* using "$tables/All_S1m_IV_pred_5_first.tex", coeflab( m_pred_lnwage "Mother's predicted log wage" m_pred_lnwage_mean_state "Mother's predicted log wage" m_pred_lnwage_mean_occ "Mother's predicted log wage" m_pred_lnwage_mean_occ_state "Mother's predicted log wage" ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m,i{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children") stats(F_all_5 N, fmt(3 0))  ti(Appendix Table: First-stage estimates for mother time/goods relative demand using different predicted wage measures as instruments (ALL, 1-2 kids ages 0-12, P(work) $\geq$ `p_work_min_m', 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(15) modelwidth(10) wrap style(tex)  /*
> */ mtitles("Uses All Variation" "Excludes Avg. State Diff." "Excludes Avg. Occ Diff." "Excludes Avg. State and Occ Diff." )  replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/All_S1m_IV_pred_5_first.tex"'})
{com}. 
. 
. 
. 
. ****  Checking no and tighter restrictions on work probability for selected specifications of mothers ****
. 
. clear 
. use "$temp/allfam_tmp.dta"
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. 
.  * No restrictions on predicted probability of work
.  
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5'  

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       860
{txt}{hline 13}{c +}{hline 34}   F(9, 850)       = {res}    20.59
{txt}       Model {c |} {res} 217.391288         9  24.1545875   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 996.974258       850  1.17291089   {txt}R-squared       ={res}    0.1790
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1703
{txt}       Total {c |} {res} 1214.36555       859  1.41369679   {txt}Root MSE        =   {res}  1.083

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6619828{col 27}{space 2} .0654514{col 38}{space 1}   10.11{col 47}{space 3}0.000{col 55}{space 4} .5335175{col 68}{space 3} .7904482
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0793583{col 27}{space 2} .0894968{col 38}{space 1}   -0.89{col 47}{space 3}0.375{col 55}{space 4}-.2550189{col 68}{space 3} .0963024
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1306852{col 27}{space 2} .0199278{col 38}{space 1}   -6.56{col 47}{space 3}0.000{col 55}{space 4}-.1697987{col 68}{space 3}-.0915717
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.0712943{col 27}{space 2} .0919934{col 38}{space 1}   -0.77{col 47}{space 3}0.439{col 55}{space 4}-.2518552{col 68}{space 3} .1092666
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2264614{col 27}{space 2} .1028981{col 38}{space 1}   -2.20{col 47}{space 3}0.028{col 55}{space 4}-.4284255{col 68}{space 3}-.0244973
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0062393{col 27}{space 2} .0068847{col 38}{space 1}   -0.91{col 47}{space 3}0.365{col 55}{space 4}-.0197523{col 68}{space 3} .0072737
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1523923{col 27}{space 2}  .083879{col 38}{space 1}   -1.82{col 47}{space 3}0.070{col 55}{space 4}-.3170264{col 68}{space 3} .0122419
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1468415{col 27}{space 2}   .10379{col 38}{space 1}    1.41{col 47}{space 3}0.157{col 55}{space 4}-.0568732{col 68}{space 3} .3505562
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1003835{col 27}{space 2} .0557032{col 38}{space 1}    1.80{col 47}{space 3}0.072{col 55}{space 4}-.0089486{col 68}{space 3} .2097155
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  1.99236{col 27}{space 2} .3272997{col 38}{space 1}    6.09{col 47}{space 3}0.000{col 55}{space 4} 1.349949{col 68}{space 3}  2.63477
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} -1.95843{col 26}{space 2} .5728508{col 37}{space 1}   -3.42{col 46}{space 3}0.001{col 54}{space 4}-3.081197{col 67}{space 3}-.8356627
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. local rhom_ols_no = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
. capture noisily test med_scoll med_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   850) ={res}    2.48
{txt}{col 13}Prob > F ={res}    0.0840
{com}. estimates store estm_ols_no
. 
. ivregress 2sls ln_tau_m_g_exp_ratio curr_married `Zm5'  (ln_wage_m_rel=m_pw_inst)
{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       851
{txt}{col 1}{col 51}Wald chi2({res}9{txt}){col 67}= {res}     83.45
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1605
{txt}{col 51}Root MSE{col 67}=    {res} 1.0868

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .4127203{col 27}{space 2} .1847733{col 38}{space 1}    2.23{col 47}{space 3}0.026{col 55}{space 4} .0505712{col 68}{space 3} .7748694
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0773869{col 27}{space 2} .0905962{col 38}{space 1}   -0.85{col 47}{space 3}0.393{col 55}{space 4}-.2549521{col 68}{space 3} .1001784
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1328559{col 27}{space 2} .0202987{col 38}{space 1}   -6.55{col 47}{space 3}0.000{col 55}{space 4}-.1726406{col 68}{space 3}-.0930711
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.0026015{col 27}{space 2} .1067829{col 38}{space 1}   -0.02{col 47}{space 3}0.981{col 55}{space 4}-.2118921{col 68}{space 3} .2066892
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.0879206{col 27}{space 2} .1493455{col 38}{space 1}   -0.59{col 47}{space 3}0.556{col 55}{space 4}-.3806323{col 68}{space 3} .2047911
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0039398{col 27}{space 2} .0071367{col 38}{space 1}   -0.55{col 47}{space 3}0.581{col 55}{space 4}-.0179275{col 68}{space 3} .0100478
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1283454{col 27}{space 2} .0860401{col 38}{space 1}   -1.49{col 47}{space 3}0.136{col 55}{space 4}-.2969809{col 68}{space 3} .0402901
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1639822{col 27}{space 2} .1056155{col 38}{space 1}    1.55{col 47}{space 3}0.121{col 55}{space 4}-.0430204{col 68}{space 3} .3709847
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0798108{col 27}{space 2}  .057738{col 38}{space 1}    1.38{col 47}{space 3}0.167{col 55}{space 4}-.0333535{col 68}{space 3} .1929751
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.500554{col 27}{space 2} .4878241{col 38}{space 1}    5.13{col 47}{space 3}0.000{col 55}{space 4} 1.544436{col 68}{space 3} 3.456671
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}curr_married age med_scoll med_collplus m_age m_white num_0_5 num_child m_pw_inst{p_end}
{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.7027661{col 26}{space 2} .5357341{col 37}{space 1}   -1.31{col 46}{space 3}0.190{col 54}{space 4}-1.752786{col 67}{space 3} .3472534
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. local rhom_ivs_no = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
. capture noisily test med_scoll med_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    0.57
{txt}{col 10}Prob > chi2 =  {res}  0.7509
{com}. estimates store estm_ivs_no
. hausman estm_ivs_no estm_ols_no, constant

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estm_ivs_no {col 30}estm_ols_no {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .4127203{col 31} .6619828{col 47}-.2492625{col 63} .1727926
{txt}curr_married {c |}{res}{col 18}-.0773869{col 31}-.0793583{col 47} .0019714{col 63} .0140709
{txt}{space 9}age {c |}{res}{col 18}-.1328559{col 31}-.1306852{col 47}-.0021707{col 63} .0038626
{txt}{space 3}med_scoll {c |}{res}{col 18}-.0026015{col 31}-.0712943{col 47} .0686928{col 63}   .05422
{txt}med_collplus {c |}{res}{col 18}-.0879206{col 31}-.2264614{col 47} .1385408{col 63} .1082407
{txt}{space 7}m_age {c |}{res}{col 18}-.0039398{col 31}-.0062393{col 47} .0022995{col 63} .0018798
{txt}{space 5}m_white {c |}{res}{col 18}-.1283454{col 31}-.1523923{col 47} .0240469{col 63} .0191629
{txt}{space 5}num_0_5 {c |}{res}{col 18} .1639822{col 31} .1468415{col 47} .0171407{col 63} .0195517
{txt}{space 3}num_child {c |}{res}{col 18} .0798108{col 31} .1003835{col 47}-.0205727{col 63} .0151928
{txt}{space 7}_cons {c |}{res}{col 18} 2.500554{col 31}  1.99236{col 47} .5081938{col 63} .3617282
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:10})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      4.68
{txt}{col 17}Prob>chi2 =  {res}    0.9116
{com}. local Hm_no = r(p)      
. 
. * Only those with very high predicted probability of work >0.85
. keep if prob_work_m >= 0.85 & prob_work_m !=.
{txt}(895 observations deleted)
{com}. 
. 
. 
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5'  

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       417
{txt}{hline 13}{c +}{hline 34}   F(9, 407)       = {res}     8.80
{txt}       Model {c |} {res} 87.5711948         9  9.73013276   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 450.072237       407  1.10582859   {txt}R-squared       ={res}    0.1629
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1444
{txt}       Total {c |} {res} 537.643432       416   1.2924121   {txt}Root MSE        =   {res} 1.0516

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6240674{col 27}{space 2} .1007834{col 38}{space 1}    6.19{col 47}{space 3}0.000{col 55}{space 4} .4259465{col 68}{space 3} .8221884
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.1460987{col 27}{space 2}  .115845{col 38}{space 1}   -1.26{col 47}{space 3}0.208{col 55}{space 4} -.373828{col 68}{space 3} .0816307
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1456018{col 27}{space 2} .0306966{col 38}{space 1}   -4.74{col 47}{space 3}0.000{col 55}{space 4}-.2059454{col 68}{space 3}-.0852582
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0208626{col 27}{space 2} .1453093{col 38}{space 1}    0.14{col 47}{space 3}0.886{col 55}{space 4}-.2647878{col 68}{space 3} .3065129
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1515837{col 27}{space 2} .1528159{col 38}{space 1}   -0.99{col 47}{space 3}0.322{col 55}{space 4}-.4519906{col 68}{space 3} .1488232
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0045206{col 27}{space 2} .0102884{col 38}{space 1}   -0.44{col 47}{space 3}0.661{col 55}{space 4}-.0247456{col 68}{space 3} .0157044
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2831554{col 27}{space 2} .1110272{col 38}{space 1}   -2.55{col 47}{space 3}0.011{col 55}{space 4}-.5014138{col 68}{space 3}-.0648971
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.0241941{col 27}{space 2} .2145487{col 38}{space 1}   -0.11{col 47}{space 3}0.910{col 55}{space 4}-.4459561{col 68}{space 3} .3975679
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1056257{col 27}{space 2} .0796462{col 38}{space 1}    1.33{col 47}{space 3}0.186{col 55}{space 4}-.0509436{col 68}{space 3}  .262195
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  2.22362{col 27}{space 2} .4607447{col 38}{space 1}    4.83{col 47}{space 3}0.000{col 55}{space 4} 1.317884{col 68}{space 3} 3.129357
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.660052{col 26}{space 2} .7131306{col 37}{space 1}   -2.33{col 46}{space 3}0.020{col 54}{space 4}-3.057762{col 67}{space 3}-.2623413
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. local rhom_ols_hi = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
. capture noisily test med_scoll med_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   407) ={res}    1.00
{txt}{col 13}Prob > F ={res}    0.3691
{com}. estimates store estm_ols_hi
. 
. ivregress 2sls ln_tau_m_g_exp_ratio curr_married `Zm5'  (ln_wage_m_rel=m_pw_inst)
{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       412
{txt}{col 1}{col 51}Wald chi2({res}9{txt}){col 67}= {res}     43.85
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1507
{txt}{col 51}Root MSE{col 67}=    {res} 1.0416

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .5314435{col 27}{space 2} .2346991{col 38}{space 1}    2.26{col 47}{space 3}0.024{col 55}{space 4} .0714417{col 68}{space 3} .9914453
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2} -.140939{col 27}{space 2}  .115932{col 38}{space 1}   -1.22{col 47}{space 3}0.224{col 55}{space 4}-.3681615{col 68}{space 3} .0862836
{txt}{space 10}age {c |}{col 15}{res}{space 2}  -.14642{col 27}{space 2} .0308713{col 38}{space 1}   -4.74{col 47}{space 3}0.000{col 55}{space 4}-.2069266{col 68}{space 3}-.0859134
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0193865{col 27}{space 2} .1462536{col 38}{space 1}    0.13{col 47}{space 3}0.895{col 55}{space 4}-.2672653{col 68}{space 3} .3060382
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} -.145013{col 27}{space 2} .1720784{col 38}{space 1}   -0.84{col 47}{space 3}0.399{col 55}{space 4}-.4822804{col 68}{space 3} .1922545
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} -.002872{col 27}{space 2} .0108111{col 38}{space 1}   -0.27{col 47}{space 3}0.791{col 55}{space 4}-.0240614{col 68}{space 3} .0183175
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2807347{col 27}{space 2} .1113335{col 38}{space 1}   -2.52{col 47}{space 3}0.012{col 55}{space 4}-.4989444{col 68}{space 3} -.062525
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} -.007134{col 27}{space 2} .2165022{col 38}{space 1}   -0.03{col 47}{space 3}0.974{col 55}{space 4}-.4314704{col 68}{space 3} .4172025
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0912001{col 27}{space 2} .0812131{col 38}{space 1}    1.12{col 47}{space 3}0.261{col 55}{space 4}-.0679746{col 68}{space 3} .2503748
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.424801{col 27}{space 2} .6272903{col 38}{space 1}    3.87{col 47}{space 3}0.000{col 55}{space 4} 1.195334{col 68}{space 3} 3.654267
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}curr_married age med_scoll med_collplus m_age m_white num_0_5 num_child m_pw_inst{p_end}
{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.134214{col 26}{space 2} 1.069024{col 37}{space 1}   -1.06{col 46}{space 3}0.289{col 54}{space 4}-3.229463{col 67}{space 3} .9610346
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. local rhom_ivs_hi = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
. capture noisily test med_scoll med_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    1.44
{txt}{col 10}Prob > chi2 =  {res}  0.4865
{com}. estimates store estm_ivs_hi
. hausman estm_ivs_hi estm_ols_hi, constant

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estm_ivs_hi {col 30}estm_ols_hi {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .5314435{col 31} .6240674{col 47}-.0926239{col 63} .2119585
{txt}curr_married {c |}{res}{col 18} -.140939{col 31}-.1460987{col 47} .0051597{col 63} .0044897
{txt}{space 9}age {c |}{res}{col 18}  -.14642{col 31}-.1456018{col 47}-.0008182{col 63} .0032799
{txt}{space 3}med_scoll {c |}{res}{col 18} .0193865{col 31} .0208626{col 47}-.0014761{col 63} .0165932
{txt}med_collplus {c |}{res}{col 18} -.145013{col 31}-.1515837{col 47} .0065708{col 63} .0791094
{txt}{space 7}m_age {c |}{res}{col 18} -.002872{col 31}-.0045206{col 47} .0016486{col 63} .0033211
{txt}{space 5}m_white {c |}{res}{col 18}-.2807347{col 31}-.2831554{col 47} .0024207{col 63}  .008253
{txt}{space 5}num_0_5 {c |}{res}{col 18} -.007134{col 31}-.0241941{col 47} .0170601{col 63} .0290178
{txt}{space 3}num_child {c |}{res}{col 18} .0912001{col 31} .1056257{col 47}-.0144256{col 63} .0158759
{txt}{space 7}_cons {c |}{res}{col 18} 2.424801{col 31}  2.22362{col 47} .2011806{col 63} .4256848
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:10})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}={res}    -8.34{txt}{col 41}chi2<0 ==> model fitted on these
{col 41}data fails to meet the asymptotic
{col 41}assumptions of the Hausman test;
{col 41}see {help suest##|_new:suest} for a generalized test
{com}. local Hm_hi = r(p)
. 
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-6: OLS & 2SLS estimates for mother time/goods relative demand for different selection
. ***  on predicted probability of work
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab estm_ols5 estm_ols_no estm_ols_hi estm_ivs5 estm_ivs_no estm_ivs_hi using "$tables/All_S1m_OLSIV_Pwork.tex", coeflab(ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m,i{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children") stats(r2 N, fmt(3 0))  ti(Appendix Table: OLS \& IV (instruments: pred wage) estimates for mother time/goods relative demand for P(work) $ \geq $ `p_work_min_m', no restriction, and P(work) $ \geq $ 0.85 (ALL, 1-2 kids ages 0-12, 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(15) modelwidth(13) wrap style(tex) mtitles("Base (OLS)" "All (OLS)" "P(work)$\geq$0.85 (OLS)" "Base (IV)" "All (IV)" "P(work)$\geq$0.85 (IV)")  replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/All_S1m_OLSIV_Pwork.tex"'})
{com}. 
. 
. 
. ****  Checking estimates for different kid ages  ****
.  
. clear 
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. *Probability of work restriction > 0.75
. keep if prob_work_m >= `p_work_min_m' & prob_work_m !=.
{txt}(356 observations deleted)
{com}. 
. 
. **All
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5'

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       727
{txt}{hline 13}{c +}{hline 34}   F(9, 717)       = {res}    18.64
{txt}       Model {c |} {res} 193.480948         9  21.4978831   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 826.887232       717  1.15325974   {txt}R-squared       ={res}    0.1896
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1794
{txt}       Total {c |} {res} 1020.36818       726  1.40546581   {txt}Root MSE        =   {res} 1.0739

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6461114{col 27}{space 2}  .071264{col 38}{space 1}    9.07{col 47}{space 3}0.000{col 55}{space 4} .5062003{col 68}{space 3} .7860226
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0737061{col 27}{space 2} .0949495{col 38}{space 1}   -0.78{col 47}{space 3}0.438{col 55}{space 4}-.2601183{col 68}{space 3} .1127061
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1410849{col 27}{space 2} .0222499{col 38}{space 1}   -6.34{col 47}{space 3}0.000{col 55}{space 4}-.1847677{col 68}{space 3}-.0974021
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0110723{col 27}{space 2} .1021967{col 38}{space 1}    0.11{col 47}{space 3}0.914{col 55}{space 4}-.1895683{col 68}{space 3} .2117129
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1571411{col 27}{space 2} .1121116{col 38}{space 1}   -1.40{col 47}{space 3}0.161{col 55}{space 4}-.3772474{col 68}{space 3} .0629652
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} -.007913{col 27}{space 2} .0075422{col 38}{space 1}   -1.05{col 47}{space 3}0.294{col 55}{space 4}-.0227205{col 68}{space 3} .0068944
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2426265{col 27}{space 2} .0894697{col 38}{space 1}   -2.71{col 47}{space 3}0.007{col 55}{space 4}-.4182804{col 68}{space 3}-.0669726
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1582784{col 27}{space 2} .1254418{col 38}{space 1}    1.26{col 47}{space 3}0.207{col 55}{space 4}-.0879988{col 68}{space 3} .4045556
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}  .089088{col 27}{space 2}  .061517{col 38}{space 1}    1.45{col 47}{space 3}0.148{col 55}{space 4}-.0316869{col 68}{space 3} .2098629
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.213075{col 27}{space 2} .3551268{col 38}{space 1}    6.23{col 47}{space 3}0.000{col 55}{space 4} 1.515863{col 68}{space 3} 2.910288
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.825748{col 26}{space 2} .5690329{col 37}{space 1}   -3.21{col 46}{space 3}0.001{col 54}{space 4}-2.941032{col 67}{space 3}-.7104642
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. estimates store estm_ols
. 
. local rssm_ols = e(rss)
. local nm_ols=e(N) 
. local k2m_ols=10
. 
. **Younger children
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5'  if age>=5 & age<=8

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       224
{txt}{hline 13}{c +}{hline 34}   F(9, 214)       = {res}     4.88
{txt}       Model {c |} {res} 43.7796631         9  4.86440701   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  213.16681       214   .99610659   {txt}R-squared       ={res}    0.1704
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1355
{txt}       Total {c |} {res} 256.946473       223  1.15222634   {txt}Root MSE        =   {res} .99805

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6481443{col 27}{space 2} .1345687{col 38}{space 1}    4.82{col 47}{space 3}0.000{col 55}{space 4} .3828944{col 68}{space 3} .9133942
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2} -.113737{col 27}{space 2} .1576832{col 38}{space 1}   -0.72{col 47}{space 3}0.472{col 55}{space 4}-.4245482{col 68}{space 3} .1970741
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.2433689{col 27}{space 2} .0794092{col 38}{space 1}   -3.06{col 47}{space 3}0.002{col 55}{space 4}-.3998934{col 68}{space 3}-.0868445
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .2627185{col 27}{space 2} .1735878{col 38}{space 1}    1.51{col 47}{space 3}0.132{col 55}{space 4}-.0794423{col 68}{space 3} .6048792
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1094654{col 27}{space 2} .1917698{col 38}{space 1}   -0.57{col 47}{space 3}0.569{col 55}{space 4} -.487465{col 68}{space 3} .2685343
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0087211{col 27}{space 2} .0123659{col 38}{space 1}   -0.71{col 47}{space 3}0.481{col 55}{space 4}-.0330956{col 68}{space 3} .0156534
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1249123{col 27}{space 2} .1438917{col 38}{space 1}   -0.87{col 47}{space 3}0.386{col 55}{space 4}-.4085387{col 68}{space 3} .1587142
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}  .129663{col 27}{space 2} .1624706{col 38}{space 1}    0.80{col 47}{space 3}0.426{col 55}{space 4}-.1905846{col 68}{space 3} .4499105
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0617191{col 27}{space 2} .1197082{col 38}{space 1}    0.52{col 47}{space 3}0.607{col 55}{space 4}-.1742391{col 68}{space 3} .2976773
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.838153{col 27}{space 2} .7439404{col 38}{space 1}    3.82{col 47}{space 3}0.000{col 55}{space 4} 1.371764{col 68}{space 3} 4.304543
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.842074{col 26}{space 2} 1.086963{col 37}{space 1}   -1.69{col 46}{space 3}0.090{col 54}{space 4}-3.972483{col 67}{space 3} .2883349
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. estimates store estm_ols_5_8
. local rssm_ols_5_8 = e(rss)
. 
. 
. **Older children
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5'  if age>=9 & age<=12

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       503
{txt}{hline 13}{c +}{hline 34}   F(9, 493)       = {res}     9.75
{txt}       Model {c |} {res} 107.985445         9  11.9983828   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 606.904002       493   1.2310426   {txt}R-squared       ={res}    0.1511
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1356
{txt}       Total {c |} {res} 714.889448       502  1.42408256   {txt}Root MSE        =   {res} 1.1095

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6587292{col 27}{space 2} .0857158{col 38}{space 1}    7.69{col 47}{space 3}0.000{col 55}{space 4} .4903158{col 68}{space 3} .8271425
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0356253{col 27}{space 2} .1196234{col 38}{space 1}   -0.30{col 47}{space 3}0.766{col 55}{space 4}-.2706599{col 68}{space 3} .1994093
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1603112{col 27}{space 2} .0478279{col 38}{space 1}   -3.35{col 47}{space 3}0.001{col 55}{space 4}-.2542828{col 68}{space 3}-.0663395
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1012522{col 27}{space 2} .1270755{col 38}{space 1}   -0.80{col 47}{space 3}0.426{col 55}{space 4}-.3509285{col 68}{space 3} .1484241
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1802784{col 27}{space 2} .1387971{col 38}{space 1}   -1.30{col 47}{space 3}0.195{col 55}{space 4}-.4529852{col 68}{space 3} .0924284
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} -.008208{col 27}{space 2}  .009598{col 38}{space 1}   -0.86{col 47}{space 3}0.393{col 55}{space 4} -.027066{col 68}{space 3} .0106499
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.3233427{col 27}{space 2} .1153734{col 38}{space 1}   -2.80{col 47}{space 3}0.005{col 55}{space 4}-.5500269{col 68}{space 3}-.0966585
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .0952611{col 27}{space 2} .2073102{col 38}{space 1}    0.46{col 47}{space 3}0.646{col 55}{space 4}-.3120593{col 68}{space 3} .5025816
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0869787{col 27}{space 2} .0728677{col 38}{space 1}    1.19{col 47}{space 3}0.233{col 55}{space 4}-.0561908{col 68}{space 3} .2301482
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.491397{col 27}{space 2} .6385588{col 38}{space 1}    3.90{col 47}{space 3}0.000{col 55}{space 4} 1.236765{col 68}{space 3} 3.746029
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.930224{col 26}{space 2} .7359743{col 37}{space 1}   -2.62{col 46}{space 3}0.009{col 54}{space 4}-3.372707{col 67}{space 3} -.487741
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. estimates store estm_ols_9_12
. local rssm_ols_9_12 = e(rss)
. 
. 
. 
. *F-stat: test difference of coefficients (younger/older children)
. 
. local Fm_ols=(`nm_ols'-2*`k2m_ols')/`k2m_ols'*(`rssm_ols'-(`rssm_ols_5_8'+`rssm_ols_9_12'))/(`rssm_ols_5_8'+`rssm_ols_9_12')
. local pm_ols=Ftail(`k2m_ols',`nm_ols'-2*`k2m_ols',`Fm_ols')
. local p: display %4.3f `pm_ols'
{res}{com}. estadd scalar p_val = `pm_ols' : estm_ols
. 
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Apendix Table G-10: OLS estimates for mother time/goods relative demand by child age
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab  estm_ols estm_ols_5_8 estm_ols_9_12 using "$tables/All_S1m_OLS_age.tex", coeflab(ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m,i{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children") stats(r2 rss N , fmt(3 3 0))  ti(Appendix Table: OLS estimates for mother time/goods relative demand by child age (ALL, 1-2 kids ages 0-12, P(work) $ \geq $ `p_work_min_m' 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(15) modelwidth(13) wrap style(tex) mtitles("All" "Ages 5-8" "Ages 9-12") addnotes("p-value for (2) and (3)= `p'") replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/All_S1m_OLS_age.tex"'})
{com}. 
. 
. 
. ****  Checking whether achievement scores 1997 AP or LW  matter ****
. 
.  
. clear 
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. *Probability of work restriction > 0.75
. keep if prob_work_m >= `p_work_min_m' & prob_work_m !=.
{txt}(356 observations deleted)
{com}. *Non-missing test scores restriction
. keep if ap97!=. & lw97 !=.
{txt}(656 observations deleted)
{com}. 
. 
. *** All
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5'  

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       339
{txt}{hline 13}{c +}{hline 34}   F(9, 329)       = {res}     3.80
{txt}       Model {c |} {res} 41.6768561         9  4.63076178   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 400.478448       329  1.21725972   {txt}R-squared       ={res}    0.0943
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0695
{txt}       Total {c |} {res} 442.155304       338  1.30815179   {txt}Root MSE        =   {res} 1.1033

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .5569262{col 27}{space 2} .1173715{col 38}{space 1}    4.74{col 47}{space 3}0.000{col 55}{space 4} .3260328{col 68}{space 3} .7878195
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.1178503{col 27}{space 2} .1473956{col 38}{space 1}   -0.80{col 47}{space 3}0.425{col 55}{space 4} -.407807{col 68}{space 3} .1721064
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.108974{col 27}{space 2} .0536492{col 38}{space 1}   -2.03{col 47}{space 3}0.043{col 55}{space 4}-.2145129{col 68}{space 3}-.0034352
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1075715{col 27}{space 2} .1548856{col 38}{space 1}   -0.69{col 47}{space 3}0.488{col 55}{space 4}-.4122626{col 68}{space 3} .1971196
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2431974{col 27}{space 2}  .171856{col 38}{space 1}   -1.42{col 47}{space 3}0.158{col 55}{space 4}-.5812726{col 68}{space 3} .0948778
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}   .00149{col 27}{space 2} .0116285{col 38}{space 1}    0.13{col 47}{space 3}0.898{col 55}{space 4}-.0213856{col 68}{space 3} .0243656
{txt}{space 6}m_white {c |}{col 15}{res}{space 2} -.278695{col 27}{space 2} .1460722{col 38}{space 1}   -1.91{col 47}{space 3}0.057{col 55}{space 4}-.5660485{col 68}{space 3} .0086584
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}  .069426{col 27}{space 2} .2319896{col 38}{space 1}    0.30{col 47}{space 3}0.765{col 55}{space 4}-.3869441{col 68}{space 3} .5257962
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1199432{col 27}{space 2} .0890342{col 38}{space 1}    1.35{col 47}{space 3}0.179{col 55}{space 4} -.055205{col 68}{space 3} .2950914
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  1.80989{col 27}{space 2} .6820638{col 38}{space 1}    2.65{col 47}{space 3}0.008{col 55}{space 4} .4681339{col 68}{space 3} 3.151647
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} -1.25696{col 26}{space 2} .5978752{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-2.428774{col 67}{space 3}-.0851462
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. estimates store estm_ols_5
. local rssm_ols_5 = e(rss)
. local nm_ols_5=e(N) 
. local k2m_ols_5=10
. 
. ***Use average numeracy (AP)/literacy (LW) score
. gen avgtest97=(ap97+lw97)/2
. 
. summ avgtest97, d

                          {txt}avgtest97
{hline 61}
      Percentiles      Smallest
 1%    {res}     -2.5      -3.966667
{txt} 5%    {res}-1.466667      -3.866667
{txt}10%    {res}    -1.05      -3.166667       {txt}Obs         {res}        500
{txt}25%    {res}-.4333333      -2.666667       {txt}Sum of Wgt. {res}        500

{txt}50%    {res}      .15                      {txt}Mean          {res}    .1608
                        {txt}Largest       Std. Dev.     {res} 1.007516
{txt}75%    {res}       .8       2.966667
{txt}90%    {res} 1.383333       3.033333       {txt}Variance      {res} 1.015089
{txt}95%    {res} 1.766667       3.266667       {txt}Skewness      {res}-.2096476
{txt}99%    {res} 2.583333            3.5       {txt}Kurtosis      {res} 4.138121
{com}. local med_avgtest97 = r(p50)
. 
. *Add test score as a control
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5' avgtest97        

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       339
{txt}{hline 13}{c +}{hline 34}   F(10, 328)      = {res}     3.43
{txt}       Model {c |} {res} 41.9047469        10  4.19047469   {txt}Prob > F        ={res}    0.0003
{txt}    Residual {c |} {res} 400.250557       328  1.22027609   {txt}R-squared       ={res}    0.0948
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0672
{txt}       Total {c |} {res} 442.155304       338  1.30815179   {txt}Root MSE        =   {res} 1.1047

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .5615499{col 27}{space 2} .1180029{col 38}{space 1}    4.76{col 47}{space 3}0.000{col 55}{space 4} .3294118{col 68}{space 3} .7936879
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.1189883{col 27}{space 2} .1476016{col 38}{space 1}   -0.81{col 47}{space 3}0.421{col 55}{space 4}-.4093536{col 68}{space 3} .1713769
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1061427{col 27}{space 2} .0541137{col 38}{space 1}   -1.96{col 47}{space 3}0.051{col 55}{space 4}-.2125965{col 68}{space 3}  .000311
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1009148{col 27}{space 2} .1558405{col 38}{space 1}   -0.65{col 47}{space 3}0.518{col 55}{space 4}-.4074878{col 68}{space 3} .2056583
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}  -.22747{col 27}{space 2} .1758754{col 38}{space 1}   -1.29{col 47}{space 3}0.197{col 55}{space 4} -.573456{col 68}{space 3}  .118516
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} .0020621{col 27}{space 2} .0117179{col 38}{space 1}    0.18{col 47}{space 3}0.860{col 55}{space 4}-.0209897{col 68}{space 3} .0251139
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2677109{col 27}{space 2} .1484454{col 38}{space 1}   -1.80{col 47}{space 3}0.072{col 55}{space 4} -.559736{col 68}{space 3} .0243142
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .0810418{col 27}{space 2} .2338269{col 38}{space 1}    0.35{col 47}{space 3}0.729{col 55}{space 4}-.3789479{col 68}{space 3} .5410314
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1176328{col 27}{space 2} .0893046{col 38}{space 1}    1.32{col 47}{space 3}0.189{col 55}{space 4}-.0580493{col 68}{space 3} .2933149
{txt}{space 4}avgtest97 {c |}{col 15}{res}{space 2}-.0292938{col 27}{space 2} .0677861{col 38}{space 1}   -0.43{col 47}{space 3}0.666{col 55}{space 4}-.1626441{col 68}{space 3} .1040566
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.741385{col 27}{space 2} .7010655{col 38}{space 1}    2.48{col 47}{space 3}0.013{col 55}{space 4}  .362233{col 68}{space 3} 3.120537
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.280761{col 26}{space 2} .6138361{col 37}{space 1}   -2.09{col 46}{space 3}0.037{col 54}{space 4}-2.483858{col 67}{space 3}-.0776645
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. estimates store estm_ols_avgtest97
. 
. *Restrict to high test score
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5' if avgtest97>`med_avgtest97' 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       174
{txt}{hline 13}{c +}{hline 34}   F(9, 164)       = {res}     4.30
{txt}       Model {c |} {res} 40.9251026         9  4.54723362   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 173.339382       164  1.05694745   {txt}R-squared       ={res}    0.1910
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1466
{txt}       Total {c |} {res} 214.264485       173  1.23852303   {txt}Root MSE        =   {res} 1.0281

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6757543{col 27}{space 2} .1422201{col 38}{space 1}    4.75{col 47}{space 3}0.000{col 55}{space 4} .3949358{col 68}{space 3} .9565729
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0536314{col 27}{space 2} .2106553{col 38}{space 1}   -0.25{col 47}{space 3}0.799{col 55}{space 4}-.4695776{col 68}{space 3} .3623149
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1207979{col 27}{space 2}  .070264{col 38}{space 1}   -1.72{col 47}{space 3}0.087{col 55}{space 4}-.2595366{col 68}{space 3} .0179408
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0898981{col 27}{space 2}  .235509{col 38}{space 1}    0.38{col 47}{space 3}0.703{col 55}{space 4}-.3751226{col 68}{space 3} .5549188
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2101008{col 27}{space 2} .2370532{col 38}{space 1}   -0.89{col 47}{space 3}0.377{col 55}{space 4}-.6781705{col 68}{space 3} .2579688
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} -.019945{col 27}{space 2} .0156245{col 38}{space 1}   -1.28{col 47}{space 3}0.204{col 55}{space 4}-.0507961{col 68}{space 3}  .010906
{txt}{space 6}m_white {c |}{col 15}{res}{space 2} .1384659{col 27}{space 2} .2101728{col 38}{space 1}    0.66{col 47}{space 3}0.511{col 55}{space 4}-.2765277{col 68}{space 3} .5534594
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2618944{col 27}{space 2} .3050137{col 38}{space 1}    0.86{col 47}{space 3}0.392{col 55}{space 4}-.3403657{col 68}{space 3} .8641545
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1304427{col 27}{space 2} .1277943{col 38}{space 1}    1.02{col 47}{space 3}0.309{col 55}{space 4}-.1218916{col 68}{space 3} .3827771
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.982524{col 27}{space 2} .9081983{col 38}{space 1}    2.18{col 47}{space 3}0.030{col 55}{space 4} .1892553{col 68}{space 3} 3.775793
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.084081{col 26}{space 2} 1.352735{col 37}{space 1}   -1.54{col 46}{space 3}0.123{col 54}{space 4}-4.735393{col 67}{space 3} .5672306
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. estimates store estm_ols_avgtest97pos
. local rssm_ols_avgtest97pos = e(rss)
. 
. *Restrict to low  test score
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married `Zm5' if avgtest97<=`med_avgtest97'    

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       165
{txt}{hline 13}{c +}{hline 34}   F(9, 155)       = {res}     1.66
{txt}       Model {c |} {res} 19.9404549         9   2.2156061   {txt}Prob > F        ={res}    0.1036
{txt}    Residual {c |} {res} 207.122418       155  1.33627366   {txt}R-squared       ={res}    0.0878
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0349
{txt}       Total {c |} {res} 227.062873       164  1.38452971   {txt}Root MSE        =   {res}  1.156

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .3266691{col 27}{space 2} .1947644{col 38}{space 1}    1.68{col 47}{space 3}0.096{col 55}{space 4} -.058066{col 68}{space 3} .7114042
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.1449853{col 27}{space 2} .2098687{col 38}{space 1}   -0.69{col 47}{space 3}0.491{col 55}{space 4}-.5595573{col 68}{space 3} .2695867
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0836455{col 27}{space 2} .0826431{col 38}{space 1}   -1.01{col 47}{space 3}0.313{col 55}{space 4}-.2468976{col 68}{space 3} .0796067
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.3025552{col 27}{space 2} .2148285{col 38}{space 1}   -1.41{col 47}{space 3}0.161{col 55}{space 4}-.7269247{col 68}{space 3} .1218142
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1125731{col 27}{space 2} .2671697{col 38}{space 1}   -0.42{col 47}{space 3}0.674{col 55}{space 4}-.6403366{col 68}{space 3} .4151904
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} .0182686{col 27}{space 2} .0172236{col 38}{space 1}    1.06{col 47}{space 3}0.290{col 55}{space 4}-.0157547{col 68}{space 3} .0522919
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.5434895{col 27}{space 2} .2123256{col 38}{space 1}   -2.56{col 47}{space 3}0.011{col 55}{space 4}-.9629146{col 68}{space 3}-.1240643
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.2222576{col 27}{space 2} .3678603{col 38}{space 1}   -0.60{col 47}{space 3}0.547{col 55}{space 4} -.948924{col 68}{space 3} .5044089
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0843295{col 27}{space 2}  .126513{col 38}{space 1}    0.67{col 47}{space 3}0.506{col 55}{space 4}-.1655826{col 68}{space 3} .3342416
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.782669{col 27}{space 2} 1.054276{col 38}{space 1}    1.69{col 47}{space 3}0.093{col 55}{space 4}-.2999343{col 68}{space 3} 3.865273
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.4851539{col 26}{space 2} .4295884{col 37}{space 1}   -1.13{col 46}{space 3}0.259{col 54}{space 4}-1.327132{col 67}{space 3} .3568239
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. estimates store estm_ols_avgtest97neg
. local rssm_ols_avgtest97neg = e(rss)
. 
. 
. *F-stat: test difference of coefficients (high/low average test score)
. 
. local Fm_ols_5=(`nm_ols_5'-2*`k2m_ols_5')/`k2m_ols_5'*(`rssm_ols_5'- (`rssm_ols_avgtest97pos' +`rssm_ols_avgtest97neg'))/(`rssm_ols_avgtest97pos' +`rssm_ols_avgtest97neg')
. local pm_ols_5=Ftail(`k2m_ols_5',`nm_ols_5'-2*`k2m_ols_5',`Fm_ols_5')
. local p: display %4.3f `pm_ols_5'
{res}{com}. estadd scalar p_val= `pm_ols_5' : estm_ols_5
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-12: OLS estimates for mother time/goods relative demand conditioning on 1997 AP and LW scores
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab estm_ols_5 estm_ols_avgtest97 estm_ols_avgtest97neg estm_ols_avgtest97pos using "$tables/All_S1m_OLS_AP_LW.tex", coeflab(ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m,i{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children") stats(r2 rss N, fmt(3 3 0))  ti(Appendix Table: OLS estimates for mother time/goods relative demand conditioning on 1997 AP and LW scores (ALL, 1-2 kids ages 0-12, P(work) $ \geq $ `p_work_min_m' 2002 and 2007, p-value `pm_ols_5')) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(15) modelwidth(13) wrap style(tex) mtitles("Baseline" "Includes Avg Test 97" "Avg Test 97 low"  "Avg Test 97 high")  replace addnotes("p-value for (3) and (4) = `p'")
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/All_S1m_OLS_AP_LW.tex"'})
{com}. 
. 
. 
. 
. *************************** SINGLE MOTHERS (2002, 2007) **************************
. 
. clear 
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. keep if curr_married==0
{txt}(1,004 observations deleted)
{com}. *Probability of work restriction > 0.75
. keep if prob_work_m >= `p_work_min_m' & prob_work_m !=.
{txt}(96 observations deleted)
{com}. 
. 
. 
. * OLS estimates
. 
. forvalues zval=1/7 {c -(}    
{txt}  2{com}.         disp _n(3) "OLS Specification " `zval' _n(2)
{txt}  3{com}.         regress ln_tau_m_g_exp_ratio  ln_wage_m_rel  `Zm`zval'' 
{txt}  4{com}.         predict PHIms_ols`zval'
{txt}  5{com}.         nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  6{com}.         capture noisily test `medvars'
{txt}  7{com}.         capture noisily test med_scoll med_collplus
{txt}  8{com}.         local rhoms_ols`zval' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  9{com}.         estimates store estms_ols`zval'
{txt} 10{com}. {c )-}



OLS Specification 1



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       236
{txt}{hline 13}{c +}{hline 34}   F(1, 234)       = {res}    25.91
{txt}       Model {c |} {res} 37.0009755         1  37.0009755   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 334.216924       234  1.42827745   {txt}R-squared       ={res}    0.0997
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0958
{txt}       Total {c |} {res} 371.217899       235  1.57965064   {txt}Root MSE        =   {res} 1.1951

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7309116{col 27}{space 2} .1436034{col 38}{space 1}    5.09{col 47}{space 3}0.000{col 55}{space 4}  .447991{col 68}{space 3} 1.013832
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .4399358{col 27}{space 2} .3587268{col 38}{space 1}    1.23{col 47}{space 3}0.221{col 55}{space 4}-.2668111{col 68}{space 3} 1.146683
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.716251{col 26}{space 2} 1.983238{col 37}{space 1}   -1.37{col 46}{space 3}0.171{col 54}{space 4}-6.603326{col 67}{space 3} 1.170823
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found



OLS Specification 2



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       236
{txt}{hline 13}{c +}{hline 34}   F(4, 231)       = {res}     7.10
{txt}       Model {c |} {res} 40.6396163         4  10.1599041   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 330.578283       231  1.43107482   {txt}R-squared       ={res}    0.1095
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0941
{txt}       Total {c |} {res} 371.217899       235  1.57965064   {txt}Root MSE        =   {res} 1.1963

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7249929{col 27}{space 2} .1555006{col 38}{space 1}    4.66{col 47}{space 3}0.000{col 55}{space 4} .4186123{col 68}{space 3} 1.031374
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2}-.1765479{col 27}{space 2} .4705593{col 38}{space 1}   -0.38{col 47}{space 3}0.708{col 55}{space 4}-1.103685{col 68}{space 3} .7505888
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0829267{col 27}{space 2} .4708773{col 38}{space 1}    0.18{col 47}{space 3}0.860{col 55}{space 4}-.8448365{col 68}{space 3}  1.01069
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1634776{col 27}{space 2} .4932304{col 38}{space 1}   -0.33{col 47}{space 3}0.741{col 55}{space 4}-1.135283{col 68}{space 3} .8083276
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .5185171{col 27}{space 2} .5629852{col 38}{space 1}    0.92{col 47}{space 3}0.358{col 55}{space 4}-.5907251{col 68}{space 3} 1.627759
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} -2.63627{col 26}{space 2}   2.0561{col 37}{space 1}   -1.28{col 46}{space 3}0.200{col 54}{space 4}-6.666152{col 67}{space 3} 1.393612
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}       F(  3,   231) ={res}    0.85
{txt}{col 13}Prob > F ={res}    0.4691

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   231) ={res}    0.65
{txt}{col 13}Prob > F ={res}    0.5239



OLS Specification 3



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       236
{txt}{hline 13}{c +}{hline 34}   F(6, 229)       = {res}     7.96
{txt}       Model {c |} {res} 64.0312732         6  10.6718789   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 307.186626       229  1.34142632   {txt}R-squared       ={res}    0.1725
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1508
{txt}       Total {c |} {res} 371.217899       235  1.57965064   {txt}Root MSE        =   {res} 1.1582

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6503109{col 27}{space 2} .1537366{col 38}{space 1}    4.23{col 47}{space 3}0.000{col 55}{space 4} .3473918{col 68}{space 3} .9532299
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1489885{col 27}{space 2} .0421925{col 38}{space 1}   -3.53{col 47}{space 3}0.001{col 55}{space 4}-.2321236{col 68}{space 3}-.0658533
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2} -.017764{col 27}{space 2}  .459923{col 38}{space 1}   -0.04{col 47}{space 3}0.969{col 55}{space 4}-.9239859{col 68}{space 3} .8884579
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .2276683{col 27}{space 2} .4586738{col 38}{space 1}    0.50{col 47}{space 3}0.620{col 55}{space 4} -.676092{col 68}{space 3} 1.131429
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.0069247{col 27}{space 2}  .481176{col 38}{space 1}   -0.01{col 47}{space 3}0.989{col 55}{space 4}-.9550229{col 68}{space 3} .9411735
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0095501{col 27}{space 2} .0132379{col 38}{space 1}   -0.72{col 47}{space 3}0.471{col 55}{space 4}-.0356337{col 68}{space 3} .0165335
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  2.34194{col 27}{space 2} .7140995{col 38}{space 1}    3.28{col 47}{space 3}0.001{col 55}{space 4}  .934895{col 68}{space 3} 3.748986
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.859683{col 26}{space 2} 1.257225{col 37}{space 1}   -1.48{col 46}{space 3}0.139{col 54}{space 4}-4.323798{col 67}{space 3} .6044324
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}       F(  3,   229) ={res}    0.81
{txt}{col 13}Prob > F ={res}    0.4873

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   229) ={res}    0.69
{txt}{col 13}Prob > F ={res}    0.5016



OLS Specification 4



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       236
{txt}{hline 13}{c +}{hline 34}   F(9, 226)       = {res}     6.18
{txt}       Model {c |} {res} 73.2721679         9  8.14135199   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 297.945732       226  1.31834394   {txt}R-squared       ={res}    0.1974
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1654
{txt}       Total {c |} {res} 371.217899       235  1.57965064   {txt}Root MSE        =   {res} 1.1482

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7114233{col 27}{space 2} .1555086{col 38}{space 1}    4.57{col 47}{space 3}0.000{col 55}{space 4}  .404991{col 68}{space 3} 1.017855
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1620733{col 27}{space 2} .0429615{col 38}{space 1}   -3.77{col 47}{space 3}0.000{col 55}{space 4}-.2467297{col 68}{space 3}-.0774169
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2}-.0079032{col 27}{space 2} .4586942{col 38}{space 1}   -0.02{col 47}{space 3}0.986{col 55}{space 4}-.9117676{col 68}{space 3} .8959612
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1909612{col 27}{space 2} .4595431{col 38}{space 1}    0.42{col 47}{space 3}0.678{col 55}{space 4} -.714576{col 68}{space 3} 1.096498
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .0012957{col 27}{space 2} .4846135{col 38}{space 1}    0.00{col 47}{space 3}0.998{col 55}{space 4}-.9536432{col 68}{space 3} .9562345
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0137145{col 27}{space 2} .0139258{col 38}{space 1}   -0.98{col 47}{space 3}0.326{col 55}{space 4}-.0411554{col 68}{space 3} .0137264
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.4134099{col 27}{space 2} .1671599{col 38}{space 1}   -2.47{col 47}{space 3}0.014{col 55}{space 4}-.7428011{col 68}{space 3}-.0840187
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.1386511{col 27}{space 2} .2406408{col 38}{space 1}   -0.58{col 47}{space 3}0.565{col 55}{space 4}-.6128377{col 68}{space 3} .3355356
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0805101{col 27}{space 2} .1096242{col 38}{space 1}    0.73{col 47}{space 3}0.463{col 55}{space 4}-.1355061{col 68}{space 3} .2965263
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.476783{col 27}{space 2} .7699209{col 38}{space 1}    3.22{col 47}{space 3}0.001{col 55}{space 4} .9596414{col 68}{space 3} 3.993925
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.465283{col 26}{space 2} 1.867376{col 37}{space 1}   -1.32{col 46}{space 3}0.187{col 54}{space 4}-6.125272{col 67}{space 3} 1.194707
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}       F(  3,   226) ={res}    0.54
{txt}{col 13}Prob > F ={res}    0.6584

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   226) ={res}    0.46
{txt}{col 13}Prob > F ={res}    0.6324



OLS Specification 5



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       236
{txt}{hline 13}{c +}{hline 34}   F(8, 227)       = {res}     6.98
{txt}       Model {c |} {res} 73.2717766         8  9.15897207   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 297.946123       227  1.31253799   {txt}R-squared       ={res}    0.1974
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1691
{txt}       Total {c |} {res} 371.217899       235  1.57965064   {txt}Root MSE        =   {res} 1.1457

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7113864{col 27}{space 2} .1551512{col 38}{space 1}    4.59{col 47}{space 3}0.000{col 55}{space 4} .4056658{col 68}{space 3} 1.017107
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1620978{col 27}{space 2} .0428433{col 38}{space 1}   -3.78{col 47}{space 3}0.000{col 55}{space 4}-.2465193{col 68}{space 3}-.0776763
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1982918{col 27}{space 2} .1732925{col 38}{space 1}    1.14{col 47}{space 3}0.254{col 55}{space 4}-.1431757{col 68}{space 3} .5397594
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .0087094{col 27}{space 2} .2224559{col 38}{space 1}    0.04{col 47}{space 3}0.969{col 55}{space 4}-.4296331{col 68}{space 3} .4470519
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0137502{col 27}{space 2}   .01374{col 38}{space 1}   -1.00{col 47}{space 3}0.318{col 55}{space 4}-.0408246{col 68}{space 3} .0133241
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.4133996{col 27}{space 2} .1667903{col 38}{space 1}   -2.48{col 47}{space 3}0.014{col 55}{space 4}-.7420548{col 68}{space 3}-.0847444
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.1390867{col 27}{space 2} .2387813{col 38}{space 1}   -0.58{col 47}{space 3}0.561{col 55}{space 4} -.609598{col 68}{space 3} .3314246
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0805871{col 27}{space 2} .1092916{col 38}{space 1}    0.74{col 47}{space 3}0.462{col 55}{space 4}-.1347686{col 68}{space 3} .2959428
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.470977{col 27}{space 2} .6907348{col 38}{space 1}    3.58{col 47}{space 3}0.000{col 55}{space 4} 1.109905{col 68}{space 3} 3.832049
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.464841{col 26}{space 2} 1.862608{col 37}{space 1}   -1.32{col 46}{space 3}0.186{col 54}{space 4}-6.115486{col 67}{space 3} 1.185805
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   227) ={res}    0.81
{txt}{col 13}Prob > F ={res}    0.4477



OLS Specification 6



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       197
{txt}{hline 13}{c +}{hline 34}   F(9, 187)       = {res}     5.06
{txt}       Model {c |} {res} 61.1801353         9  6.79779281   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  251.13737       187  1.34298059   {txt}R-squared       ={res}    0.1959
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1572
{txt}       Total {c |} {res} 312.317506       196  1.59345666   {txt}Root MSE        =   {res} 1.1589

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7398682{col 27}{space 2} .1742814{col 38}{space 1}    4.25{col 47}{space 3}0.000{col 55}{space 4} .3960578{col 68}{space 3} 1.083679
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1816478{col 27}{space 2} .0485372{col 38}{space 1}   -3.74{col 47}{space 3}0.000{col 55}{space 4}-.2773986{col 68}{space 3}-.0858971
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1128552{col 27}{space 2} .1995935{col 38}{space 1}    0.57{col 47}{space 3}0.572{col 55}{space 4}-.2808891{col 68}{space 3} .5065995
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.0838736{col 27}{space 2} .2587548{col 38}{space 1}   -0.32{col 47}{space 3}0.746{col 55}{space 4}-.5943273{col 68}{space 3} .4265801
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0088443{col 27}{space 2} .0150812{col 38}{space 1}   -0.59{col 47}{space 3}0.558{col 55}{space 4}-.0385954{col 68}{space 3} .0209068
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0638549{col 27}{space 2} .1960981{col 38}{space 1}   -0.33{col 47}{space 3}0.745{col 55}{space 4}-.4507037{col 68}{space 3} .3229939
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.0296297{col 27}{space 2} .2581736{col 38}{space 1}   -0.11{col 47}{space 3}0.909{col 55}{space 4}-.5389367{col 68}{space 3} .4796774
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1202436{col 27}{space 2} .1186954{col 38}{space 1}    1.01{col 47}{space 3}0.312{col 55}{space 4}-.1139106{col 68}{space 3} .3543978
{txt}{space 7}m_pc97 {c |}{col 15}{res}{space 2} -.009492{col 27}{space 2} .0061231{col 38}{space 1}   -1.55{col 47}{space 3}0.123{col 55}{space 4}-.0215713{col 68}{space 3} .0025872
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.132998{col 27}{space 2} .8894525{col 38}{space 1}    3.52{col 47}{space 3}0.001{col 55}{space 4} 1.378348{col 68}{space 3} 4.887649
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(146 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.844205{col 26}{space 2} 2.575516{col 37}{space 1}   -1.10{col 46}{space 3}0.269{col 54}{space 4}-7.892123{col 67}{space 3} 2.203713
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   187) ={res}    0.41
{txt}{col 13}Prob > F ={res}    0.6639



OLS Specification 7



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       162
{txt}{hline 13}{c +}{hline 34}   F(6, 155)       = {res}     7.06
{txt}       Model {c |} {res}  51.758033         6  8.62633883   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 189.489104       155  1.22251035   {txt}R-squared       ={res}    0.2145
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1841
{txt}       Total {c |} {res} 241.247137       161  1.49842942   {txt}Root MSE        =   {res} 1.1057

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7670501{col 27}{space 2} .1975086{col 38}{space 1}    3.88{col 47}{space 3}0.000{col 55}{space 4} .3768941{col 68}{space 3} 1.157206
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1630642{col 27}{space 2} .0533643{col 38}{space 1}   -3.06{col 47}{space 3}0.003{col 55}{space 4}-.2684794{col 68}{space 3}-.0576489
{txt}{space 2}ln_wageFE_m {c |}{col 15}{res}{space 2}-.0888207{col 27}{space 2} .1976149{col 38}{space 1}   -0.45{col 47}{space 3}0.654{col 55}{space 4}-.4791866{col 68}{space 3} .3015452
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.5794787{col 27}{space 2} .1921161{col 38}{space 1}   -3.02{col 47}{space 3}0.003{col 55}{space 4}-.9589823{col 68}{space 3} -.199975
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.0106663{col 27}{space 2} .3600171{col 38}{space 1}   -0.03{col 47}{space 3}0.976{col 55}{space 4}-.7218396{col 68}{space 3} .7005069
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}-.0105918{col 27}{space 2} .1174858{col 38}{space 1}   -0.09{col 47}{space 3}0.928{col 55}{space 4}-.2426717{col 68}{space 3} .2214881
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.054826{col 27}{space 2}  .829137{col 38}{space 1}    2.48{col 47}{space 3}0.014{col 55}{space 4} .4169594{col 68}{space 3} 3.692693
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(186 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-3.292768{col 26}{space 2}  3.63966{col 37}{space 1}   -0.90{col 46}{space 3}0.366{col 54}{space 4}-10.42637{col 67}{space 3} 3.840834
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found
{com}. 
. 
. * IV estimates: state of residence
. 
. forvalues zval=1/7 {c -(}
{txt}  2{com}.         disp _n(3) "IV Specification " `zval' _n(2)
{txt}  3{com}.         ivregress 2sls ln_tau_m_g_exp_ratio `Zm`zval''  (ln_wage_m_rel=i.state i.year)
{txt}  4{com}.         predict PHIms_ivs`zval'
{txt}  5{com}.         nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  6{com}.         capture noisily test `medvars'
{txt}  7{com}.         capture noisily test med_scoll med_collplus
{txt}  8{com}.         local rhoms_ivs`zval' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  9{com}.         estimates store estms_ivs`zval'
{txt} 10{com}.         estat firststage
{txt} 11{com}.         hausman estms_ivs`zval' estms_ols`zval', constant
{txt} 12{com}.         local Hm`zval' = r(p)
{txt} 13{com}. {c )-}



IV Specification 1


{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       236
{txt}{col 1}{col 51}Wald chi2({res}1{txt}){col 67}= {res}     14.00
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0002
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.0882
{txt}{col 51}Root MSE{col 67}=    {res} 1.1976

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .9783932{col 27}{space 2} .2614887{col 38}{space 1}    3.74{col 47}{space 3}0.000{col 55}{space 4} .4658848{col 68}{space 3} 1.490902
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.1635703{col 27}{space 2} .6424111{col 38}{space 1}   -0.25{col 47}{space 3}0.799{col 55}{space 4}-1.422673{col 68}{space 3} 1.095532
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}4.state 5.state 6.state 8.state 9.state 11.state 12.state 13.state 16.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 32.state 34.state 36.state 37.state 39.state 40.state 41.state 42.state 45.state 47.state 48.state 49.state 51.state 53.state 2007.year{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-45.28163{col 26}{space 2}  560.106{col 37}{space 1}   -0.08{col 46}{space 3}0.936{col 54}{space 4}-1143.069{col 67}{space 3} 1052.506
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(35,200){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.3028{col 29}{space 2}0.1808{col 41}{space 3}0.3028{col 53}{space 3} 2.48217{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}2.48217     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   35
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estms_ivs1 {col 30} estms_ols1 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .9783932{col 31} .7309116{col 47} .2474815{col 63} .2185278
{txt}{space 7}_cons {c |}{res}{col 18}-.1635703{col 31} .4399358{col 47}-.6035061{col 63} .5329231
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:2})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      1.28
{txt}{col 17}Prob>chi2 =  {res}    0.5266



IV Specification 2



{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       236
{txt}{col 1}{col 51}Wald chi2({res}4{txt}){col 67}= {res}     20.14
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0005
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.0891
{txt}{col 51}Root MSE{col 67}=    {res}  1.197

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} 1.082512{col 27}{space 2} .2947912{col 38}{space 1}    3.67{col 47}{space 3}0.000{col 55}{space 4} .5047323{col 68}{space 3} 1.660293
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2}-.2047636{col 27}{space 2} .4712589{col 38}{space 1}   -0.43{col 47}{space 3}0.664{col 55}{space 4}-1.128414{col 68}{space 3} .7188869
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.0254565{col 27}{space 2} .4772376{col 38}{space 1}   -0.05{col 47}{space 3}0.957{col 55}{space 4}-.9608251{col 68}{space 3}  .909912
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.3891151{col 27}{space 2} .5182105{col 38}{space 1}   -0.75{col 47}{space 3}0.453{col 55}{space 4}-1.404789{col 68}{space 3} .6265589
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.2526869{col 27}{space 2} .7804147{col 38}{space 1}   -0.32{col 47}{space 3}0.746{col 55}{space 4}-1.782272{col 68}{space 3} 1.276898
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}med_hs med_scoll med_collplus 4.state 5.state 6.state 8.state 9.state 11.state 12.state 13.state 16.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 32.state 34.state 36.state 37.state 39.state 40.state 41.state 42.state 45.state 47.state 48.state 49.state 51.state 53.state 2007.year{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} 13.11938{col 26}{space 2} 43.29874{col 37}{space 1}    0.30{col 46}{space 3}0.762{col 54}{space 4} -71.7446{col 67}{space 3} 97.98335
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  3) ={res}    2.97
{txt}{col 10}Prob > chi2 =  {res}  0.3966

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    2.49
{txt}{col 10}Prob > chi2 =  {res}  0.2886

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(35,197){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.3835{col 29}{space 2}0.2646{col 41}{space 3}0.2786{col 53}{space 3} 2.17358{col 65}{space 3}0.0005
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}2.17358     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   35
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estms_ivs2 {col 30} estms_ols2 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} 1.082512{col 31} .7249929{col 47} .3575196{col 63} .2504425
{txt}{space 6}med_hs {c |}{res}{col 18}-.2047636{col 31}-.1765479{col 47}-.0282157{col 63} .0256693
{txt}{space 3}med_scoll {c |}{res}{col 18}-.0254565{col 31} .0829267{col 47}-.1083833{col 63} .0776552
{txt}med_collplus {c |}{res}{col 18}-.3891151{col 31}-.1634776{col 47}-.2256374{col 63} .1589526
{txt}{space 7}_cons {c |}{res}{col 18}-.2526869{col 31} .5185171{col 47}-.7712041{col 63} .5404579
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:5})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      2.04
{txt}{col 17}Prob>chi2 =  {res}    0.8439



IV Specification 3



{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       236
{txt}{col 1}{col 51}Wald chi2({res}6{txt}){col 67}= {res}     40.28
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1593
{txt}{col 51}Root MSE{col 67}=    {res} 1.1499

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .9435967{col 27}{space 2} .2982133{col 38}{space 1}    3.16{col 47}{space 3}0.002{col 55}{space 4} .3591093{col 68}{space 3} 1.528084
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1339483{col 27}{space 2} .0439028{col 38}{space 1}   -3.05{col 47}{space 3}0.002{col 55}{space 4}-.2199962{col 68}{space 3}-.0479004
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2}-.0324986{col 27}{space 2}  .456818{col 38}{space 1}   -0.07{col 47}{space 3}0.943{col 55}{space 4}-.9278455{col 68}{space 3} .8628483
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1463074{col 27}{space 2} .4609086{col 38}{space 1}    0.32{col 47}{space 3}0.751{col 55}{space 4}-.7570568{col 68}{space 3} 1.049672
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1783005{col 27}{space 2} .5006427{col 38}{space 1}   -0.36{col 47}{space 3}0.722{col 55}{space 4}-1.159542{col 68}{space 3} .8029412
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0131658{col 27}{space 2} .0135174{col 38}{space 1}   -0.97{col 47}{space 3}0.330{col 55}{space 4}-.0396595{col 68}{space 3} .0133278
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.685422{col 27}{space 2} .9118955{col 38}{space 1}    1.85{col 47}{space 3}0.065{col 55}{space 4}-.1018607{col 68}{space 3} 3.472704
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_hs med_scoll med_collplus m_age 4.state 5.state 6.state 8.state 9.state 11.state 12.state 13.state 16.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 32.state 34.state 36.state 37.state 39.state 40.state 41.state 42.state 45.state 47.state 48.state 49.state 51.state 53.state 2007.year{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-16.72945{col 26}{space 2} 93.73843{col 37}{space 1}   -0.18{col 46}{space 3}0.858{col 54}{space 4}-200.4534{col 67}{space 3} 166.9945
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  3) ={res}    2.63
{txt}{col 10}Prob > chi2 =  {res}  0.4528

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    2.18
{txt}{col 10}Prob > chi2 =  {res}  0.3360

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(35,195){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.3952{col 29}{space 2}0.2712{col 41}{space 3}0.2620{col 53}{space 3} 1.97775{col 65}{space 3}0.0020
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}1.97775     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   35
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estms_ivs3 {col 30} estms_ols3 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .9435967{col 31} .6503109{col 47} .2932858{col 63} .2555313
{txt}{space 9}age {c |}{res}{col 18}-.1339483{col 31}-.1489885{col 47} .0150402{col 63} .0121347
{txt}{space 6}med_hs {c |}{res}{col 18}-.0324986{col 31} -.017764{col 47}-.0147346{col 63}        .
{txt}{space 3}med_scoll {c |}{res}{col 18} .1463074{col 31} .2276683{col 47}-.0813609{col 63} .0453333
{txt}med_collplus {c |}{res}{col 18}-.1783005{col 31}-.0069247{col 47}-.1713758{col 63} .1382491
{txt}{space 7}m_age {c |}{res}{col 18}-.0131658{col 31}-.0095501{col 47}-.0036157{col 63} .0027349
{txt}{space 7}_cons {c |}{res}{col 18} 1.685422{col 31}  2.34194{col 47}-.6565188{col 63} .5671114
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:7})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      1.32
{txt}{col 17}Prob>chi2 =  {res}    0.9880
{txt}{col 17}(V_b-V_B is not positive definite)



IV Specification 4


{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       236
{txt}{col 1}{col 51}Wald chi2({res}9{txt}){col 67}= {res}     47.09
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1830
{txt}{col 51}Root MSE{col 67}=    {res} 1.1336

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} 1.023878{col 27}{space 2} .3013931{col 38}{space 1}    3.40{col 47}{space 3}0.001{col 55}{space 4}  .433158{col 68}{space 3} 1.614597
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1488388{col 27}{space 2} .0438148{col 38}{space 1}   -3.40{col 47}{space 3}0.001{col 55}{space 4}-.2347143{col 68}{space 3}-.0629634
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2}-.0205663{col 27}{space 2} .4529843{col 38}{space 1}   -0.05{col 47}{space 3}0.964{col 55}{space 4}-.9083991{col 68}{space 3} .8672665
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1035678{col 27}{space 2} .4594632{col 38}{space 1}    0.23{col 47}{space 3}0.822{col 55}{space 4}-.7969634{col 68}{space 3} 1.004099
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1690882{col 27}{space 2} .4989175{col 38}{space 1}   -0.34{col 47}{space 3}0.735{col 55}{space 4}-1.146948{col 68}{space 3}  .808772
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0177957{col 27}{space 2} .0141599{col 38}{space 1}   -1.26{col 47}{space 3}0.209{col 55}{space 4}-.0455486{col 68}{space 3} .0099572
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.4444386{col 27}{space 2} .1670322{col 38}{space 1}   -2.66{col 47}{space 3}0.008{col 55}{space 4}-.7718157{col 68}{space 3}-.1170615
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.1455198{col 27}{space 2} .2376497{col 38}{space 1}   -0.61{col 47}{space 3}0.540{col 55}{space 4}-.6113045{col 68}{space 3}  .320265
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1164539{col 27}{space 2} .1122675{col 38}{space 1}    1.04{col 47}{space 3}0.300{col 55}{space 4}-.1035863{col 68}{space 3}  .336494
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.751465{col 27}{space 2} .9696785{col 38}{space 1}    1.81{col 47}{space 3}0.071{col 55}{space 4}-.1490704{col 68}{space 3}    3.652
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_hs med_scoll med_collplus m_age m_white num_0_5 num_child 4.state 5.state 6.state 8.state 9.state 11.state 12.state 13.state 16.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 32.state 34.state 36.state 37.state 39.state 40.state 41.state 42.state 45.state 47.state 48.state 49.state 51.state 53.state 2007.year{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}  42.8803{col 26}{space 2} 528.6313{col 37}{space 1}    0.08{col 46}{space 3}0.935{col 54}{space 4}-993.2179{col 67}{space 3} 1078.979
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  3) ={res}    1.75
{txt}{col 10}Prob > chi2 =  {res}  0.6258

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    1.58
{txt}{col 10}Prob > chi2 =  {res}  0.4532

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(35,192){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.4171{col 29}{space 2}0.2866{col 41}{space 3}0.2595{col 53}{space 3} 1.92235{col 65}{space 3}0.0029
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}1.92235     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   35
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estms_ivs4 {col 30} estms_ols4 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} 1.023878{col 31} .7114233{col 47} .3124543{col 63}  .258176
{txt}{space 9}age {c |}{res}{col 18}-.1488388{col 31}-.1620733{col 47} .0132344{col 63} .0086049
{txt}{space 6}med_hs {c |}{res}{col 18}-.0205663{col 31}-.0079032{col 47}-.0126631{col 63}        .
{txt}{space 3}med_scoll {c |}{res}{col 18} .1035678{col 31} .1909612{col 47}-.0873934{col 63}        .
{txt}med_collplus {c |}{res}{col 18}-.1690882{col 31} .0012957{col 47}-.1703839{col 63} .1186101
{txt}{space 7}m_age {c |}{res}{col 18}-.0177957{col 31}-.0137145{col 47}-.0040812{col 63} .0025644
{txt}{space 5}m_white {c |}{res}{col 18}-.4444386{col 31}-.4134099{col 47}-.0310287{col 63}        .
{txt}{space 5}num_0_5 {c |}{res}{col 18}-.1455198{col 31}-.1386511{col 47}-.0068687{col 63}        .
{txt}{space 3}num_child {c |}{res}{col 18} .1164539{col 31} .0805101{col 47} .0359438{col 63} .0242182
{txt}{space 7}_cons {c |}{res}{col 18} 1.751465{col 31} 2.476783{col 47}-.7253183{col 63} .5894899
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:10})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      1.46
{txt}{col 17}Prob>chi2 =  {res}    0.9990
{txt}{col 17}(V_b-V_B is not positive definite)



IV Specification 5


{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       236
{txt}{col 1}{col 51}Wald chi2({res}8{txt}){col 67}= {res}     47.02
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1834
{txt}{col 51}Root MSE{col 67}=    {res} 1.1334

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} 1.020179{col 27}{space 2} .3013131{col 38}{space 1}    3.39{col 47}{space 3}0.001{col 55}{space 4} .4296159{col 68}{space 3} 1.610742
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1490548{col 27}{space 2} .0437757{col 38}{space 1}   -3.40{col 47}{space 3}0.001{col 55}{space 4}-.2348535{col 68}{space 3}-.0632561
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1235165{col 27}{space 2} .1825691{col 38}{space 1}    0.68{col 47}{space 3}0.499{col 55}{space 4}-.2343123{col 68}{space 3} .4813454
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1479676{col 27}{space 2} .2563946{col 38}{space 1}   -0.58{col 47}{space 3}0.564{col 55}{space 4}-.6504918{col 68}{space 3} .3545566
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0178409{col 27}{space 2} .0140199{col 38}{space 1}   -1.27{col 47}{space 3}0.203{col 55}{space 4}-.0453194{col 68}{space 3} .0096375
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.4440541{col 27}{space 2} .1669958{col 38}{space 1}   -2.66{col 47}{space 3}0.008{col 55}{space 4}-.7713599{col 68}{space 3}-.1167483
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.1465661{col 27}{space 2} .2363019{col 38}{space 1}   -0.62{col 47}{space 3}0.535{col 55}{space 4}-.6097094{col 68}{space 3} .3165772
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1162383{col 27}{space 2} .1121864{col 38}{space 1}    1.04{col 47}{space 3}0.300{col 55}{space 4} -.103643{col 68}{space 3} .3361196
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.744828{col 27}{space 2} .9158125{col 38}{space 1}    1.91{col 47}{space 3}0.057{col 55}{space 4}-.0501311{col 68}{space 3} 3.539788
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_scoll med_collplus m_age m_white num_0_5 num_child 4.state 5.state 6.state 8.state 9.state 11.state 12.state 13.state 16.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 32.state 34.state 36.state 37.state 39.state 40.state 41.state 42.state 45.state 47.state 48.state 49.state 51.state 53.state 2007.year{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} 50.55696{col 26}{space 2} 739.9927{col 37}{space 1}    0.07{col 46}{space 3}0.946{col 54}{space 4}-1399.802{col 67}{space 3} 1500.916
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    1.74
{txt}{col 10}Prob > chi2 =  {res}  0.4182

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(35,193){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.4170{col 29}{space 2}0.2901{col 41}{space 3}0.2595{col 53}{space 3}  1.9322{col 65}{space 3}0.0027
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}1.9322      

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   35
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estms_ivs5 {col 30} estms_ols5 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} 1.020179{col 31} .7113864{col 47} .3087924{col 63} .2582977
{txt}{space 9}age {c |}{res}{col 18}-.1490548{col 31}-.1620978{col 47}  .013043{col 63} .0089865
{txt}{space 3}med_scoll {c |}{res}{col 18} .1235165{col 31} .1982918{col 47}-.0747753{col 63}  .057456
{txt}med_collplus {c |}{res}{col 18}-.1479676{col 31} .0087094{col 47} -.156677{col 63} .1274817
{txt}{space 7}m_age {c |}{res}{col 18}-.0178409{col 31}-.0137502{col 47}-.0040907{col 63} .0027871
{txt}{space 5}m_white {c |}{res}{col 18}-.4440541{col 31}-.4133996{col 47}-.0306545{col 63} .0082826
{txt}{space 5}num_0_5 {c |}{res}{col 18}-.1465661{col 31}-.1390867{col 47}-.0074794{col 63}        .
{txt}{space 3}num_child {c |}{res}{col 18} .1162383{col 31} .0805871{col 47} .0356512{col 63} .0253207
{txt}{space 7}_cons {c |}{res}{col 18} 1.744828{col 31} 2.470977{col 47}-.7261485{col 63} .6013302
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:9})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      1.43
{txt}{col 17}Prob>chi2 =  {res}    0.9976
{txt}{col 17}(V_b-V_B is not positive definite)



IV Specification 6


{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       197
{txt}{col 1}{col 51}Wald chi2({res}9{txt}){col 67}= {res}     39.68
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1766
{txt}{col 51}Root MSE{col 67}=    {res} 1.1426

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} 1.109389{col 27}{space 2}  .329179{col 38}{space 1}    3.37{col 47}{space 3}0.001{col 55}{space 4} .4642103{col 68}{space 3} 1.754568
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1643253{col 27}{space 2} .0496314{col 38}{space 1}   -3.31{col 47}{space 3}0.001{col 55}{space 4} -.261601{col 68}{space 3}-.0670496
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}  .008527{col 27}{space 2} .2121519{col 38}{space 1}    0.04{col 47}{space 3}0.968{col 55}{space 4}-.4072831{col 68}{space 3} .4243371
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} -.296828{col 27}{space 2} .3021017{col 38}{space 1}   -0.98{col 47}{space 3}0.326{col 55}{space 4}-.8889365{col 68}{space 3} .2952804
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0138689{col 27}{space 2} .0153513{col 38}{space 1}   -0.90{col 47}{space 3}0.366{col 55}{space 4}-.0439568{col 68}{space 3} .0162191
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1041722{col 27}{space 2}  .195751{col 38}{space 1}   -0.53{col 47}{space 3}0.595{col 55}{space 4} -.487837{col 68}{space 3} .2794927
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.0403048{col 27}{space 2} .2546703{col 38}{space 1}   -0.16{col 47}{space 3}0.874{col 55}{space 4}-.5394495{col 68}{space 3} .4588399
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1553425{col 27}{space 2} .1200258{col 38}{space 1}    1.29{col 47}{space 3}0.196{col 55}{space 4}-.0799037{col 68}{space 3} .3905888
{txt}{space 7}m_pc97 {c |}{col 15}{res}{space 2}-.0098836{col 27}{space 2} .0060443{col 38}{space 1}   -1.64{col 47}{space 3}0.102{col 55}{space 4}-.0217302{col 68}{space 3}  .001963
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.327299{col 27}{space 2} 1.069487{col 38}{space 1}    2.18{col 47}{space 3}0.030{col 55}{space 4} .2311439{col 68}{space 3} 4.423455
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_scoll med_collplus m_age m_white num_0_5 num_child m_pc97 4.state 5.state 6.state 8.state 9.state 11.state 12.state 13.state 16.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 34.state 36.state 37.state 39.state 41.state 42.state 45.state 47.state 48.state 49.state 51.state 53.state 2007.year{p_end}
(option {bf:xb} assumed; fitted values)
(146 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} 10.14167{col 26}{space 2} 27.50953{col 37}{space 1}    0.37{col 46}{space 3}0.712{col 54}{space 4}-43.77602{col 67}{space 3} 64.05936
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    1.58
{txt}{col 10}Prob > chi2 =  {res}  0.4549

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(33,155){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.4663{col 29}{space 2}0.3251{col 41}{space 3}0.2725{col 53}{space 3} 1.75915{col 65}{space 3}0.0119
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}1.75915     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   33
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estms_ivs6 {col 30} estms_ols6 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} 1.109389{col 31} .7398682{col 47}  .369521{col 63} .2792576
{txt}{space 9}age {c |}{res}{col 18}-.1643253{col 31}-.1816478{col 47} .0173225{col 63} .0103642
{txt}{space 3}med_scoll {c |}{res}{col 18}  .008527{col 31} .1128552{col 47}-.1043282{col 63} .0719087
{txt}med_collplus {c |}{res}{col 18} -.296828{col 31}-.0838736{col 47}-.2129545{col 63}  .155921
{txt}{space 7}m_age {c |}{res}{col 18}-.0138689{col 31}-.0088443{col 47}-.0050246{col 63} .0028672
{txt}{space 5}m_white {c |}{res}{col 18}-.1041722{col 31}-.0638549{col 47}-.0403173{col 63}        .
{txt}{space 5}num_0_5 {c |}{res}{col 18}-.0403048{col 31}-.0296297{col 47}-.0106752{col 63}        .
{txt}{space 3}num_child {c |}{res}{col 18} .1553425{col 31} .1202436{col 47} .0350989{col 63} .0178209
{txt}{space 6}m_pc97 {c |}{res}{col 18}-.0098836{col 31} -.009492{col 47}-.0003916{col 63}        .
{txt}{space 7}_cons {c |}{res}{col 18} 2.327299{col 31} 3.132998{col 47} -.805699{col 63} .5938653
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:10})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      1.75
{txt}{col 17}Prob>chi2 =  {res}    0.9979
{txt}{col 17}(V_b-V_B is not positive definite)



IV Specification 7


{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       162
{txt}{col 1}{col 51}Wald chi2({res}6{txt}){col 67}= {res}     29.96
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.2046
{txt}{col 51}Root MSE{col 67}=    {res} 1.0884

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .4899393{col 27}{space 2} .3617092{col 38}{space 1}    1.35{col 47}{space 3}0.176{col 55}{space 4}-.2189977{col 68}{space 3} 1.198876
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1732069{col 27}{space 2} .0537024{col 38}{space 1}   -3.23{col 47}{space 3}0.001{col 55}{space 4}-.2784616{col 68}{space 3}-.0679522
{txt}{space 2}ln_wageFE_m {c |}{col 15}{res}{space 2} .0572248{col 27}{space 2} .2523495{col 38}{space 1}    0.23{col 47}{space 3}0.821{col 55}{space 4}-.4373711{col 68}{space 3} .5518206
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.5615316{col 27}{space 2} .1901381{col 38}{space 1}   -2.95{col 47}{space 3}0.003{col 55}{space 4}-.9341954{col 68}{space 3}-.1888677
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.0112112{col 27}{space 2} .3543827{col 38}{space 1}   -0.03{col 47}{space 3}0.975{col 55}{space 4}-.7057885{col 68}{space 3} .6833662
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}-.0366125{col 27}{space 2} .1191408{col 38}{space 1}   -0.31{col 47}{space 3}0.759{col 55}{space 4}-.2701241{col 68}{space 3} .1968991
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  2.90129{col 27}{space 2} 1.238626{col 38}{space 1}    2.34{col 47}{space 3}0.019{col 55}{space 4} .4736274{col 68}{space 3} 5.328953
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age ln_wageFE_m m_white num_0_5 num_child 4.state 5.state 6.state 8.state 9.state 11.state 12.state 13.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 32.state 34.state 36.state 37.state 39.state 40.state 41.state 42.state 45.state 47.state 48.state 49.state 51.state 53.state 2007.year{p_end}
(option {bf:xb} assumed; fitted values)
(186 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.9605509{col 26}{space 2} 1.390323{col 37}{space 1}   -0.69{col 46}{space 3}0.490{col 54}{space 4}-3.685535{col 67}{space 3} 1.764433
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(34,122){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.5366{col 29}{space 2}0.3885{col 41}{space 3}0.2889{col 53}{space 3} 1.45781{col 65}{space 3}0.0711
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}1.45781     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   34
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estms_ivs7 {col 30} estms_ols7 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .4899393{col 31} .7670501{col 47}-.2771108{col 63} .3030246
{txt}{space 9}age {c |}{res}{col 18}-.1732069{col 31}-.1630642{col 47}-.0101427{col 63} .0060158
{txt}{space 1}ln_wageFE_m {c |}{res}{col 18} .0572248{col 31}-.0888207{col 47} .1460454{col 63} .1569351
{txt}{space 5}m_white {c |}{res}{col 18}-.5615316{col 31}-.5794787{col 47} .0179471{col 63}        .
{txt}{space 5}num_0_5 {c |}{res}{col 18}-.0112112{col 31}-.0106663{col 47}-.0005448{col 63}        .
{txt}{space 3}num_child {c |}{res}{col 18}-.0366125{col 31}-.0105918{col 47}-.0260207{col 63} .0197892
{txt}{space 7}_cons {c |}{res}{col 18}  2.90129{col 31} 2.054826{col 47} .8464643{col 63} .9201777
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:7})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      0.84
{txt}{col 17}Prob>chi2 =  {res}    0.9971
{txt}{col 17}(V_b-V_B is not positive definite)
{com}. 
. * IV estimates: predicted wages
. 
. forvalues zval=1/7 {c -(}
{txt}  2{com}.         disp _n(3) "IV Specification " `zval' _n(2)
{txt}  3{com}.         ivregress 2sls ln_tau_m_g_exp_ratio `Zm`zval''  (ln_wage_m_rel=m_pw_inst)
{txt}  4{com}.         predict PHImpw_ivpw`zval'
{txt}  5{com}.         nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  6{com}.         capture noisily test `medvars'
{txt}  7{com}.         capture noisily test med_scoll med_collplus
{txt}  8{com}.         local rhoms_ivpw`zval' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  9{com}.         estimates store estms_ivpw`zval'
{txt} 10{com}.         estat firststage
{txt} 11{com}.         hausman estms_ivpw`zval' estms_ols`zval', constant
{txt} 12{com}.         local Hmpw`zval' = r(p)
{txt} 13{com}. {c )-}



IV Specification 1


{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       233
{txt}{col 1}{col 51}Wald chi2({res}1{txt}){col 67}= {res}      1.52
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.2170
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.0626
{txt}{col 51}Root MSE{col 67}=    {res} 1.2038

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .3752836{col 27}{space 2} .3039852{col 38}{space 1}    1.23{col 47}{space 3}0.217{col 55}{space 4}-.2205165{col 68}{space 3} .9710837
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.308922{col 27}{space 2} .7470618{col 38}{space 1}    1.75{col 47}{space 3}0.080{col 55}{space 4}-.1552925{col 68}{space 3} 2.773136
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}m_pw_inst{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.6007264{col 26}{space 2} .7789089{col 37}{space 1}   -0.77{col 46}{space 3}0.441{col 54}{space 4} -2.12736{col 67}{space 3}  .925907
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,231){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.2540{col 29}{space 2}0.2508{col 41}{space 3}0.2540{col 53}{space 3} 78.6559{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}78.6559     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estms_ivpw1 {col 30} estms_ols1 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .3752836{col 31} .7309116{col 47} -.355628{col 63} .2679274
{txt}{space 7}_cons {c |}{res}{col 18} 1.308922{col 31} .4399358{col 47}  .868986{col 63} .6552987
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:2})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      1.76
{txt}{col 17}Prob>chi2 =  {res}    0.4139



IV Specification 2



{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       233
{txt}{col 1}{col 51}Wald chi2({res}4{txt}){col 67}= {res}      6.20
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.1846
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.0721
{txt}{col 51}Root MSE{col 67}=    {res} 1.1976

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .3439922{col 27}{space 2} .3636705{col 38}{space 1}    0.95{col 47}{space 3}0.344{col 55}{space 4}-.3687888{col 68}{space 3} 1.056773
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2}-.1238576{col 27}{space 2} .4727546{col 38}{space 1}   -0.26{col 47}{space 3}0.793{col 55}{space 4} -1.05044{col 68}{space 3} .8027243
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1984284{col 27}{space 2} .4818323{col 38}{space 1}    0.41{col 47}{space 3}0.680{col 55}{space 4}-.7459456{col 68}{space 3} 1.142802
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .0434692{col 27}{space 2} .5346736{col 38}{space 1}    0.08{col 47}{space 3}0.935{col 55}{space 4}-1.004472{col 68}{space 3}  1.09141
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.340372{col 27}{space 2} .9057074{col 38}{space 1}    1.48{col 47}{space 3}0.139{col 55}{space 4}-.4347815{col 68}{space 3} 3.115526
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}med_hs med_scoll med_collplus m_pw_inst{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.5243721{col 26}{space 2} .8450649{col 37}{space 1}   -0.62{col 46}{space 3}0.535{col 54}{space 4}-2.180669{col 67}{space 3} 1.131925
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  3) ={res}    2.94
{txt}{col 10}Prob > chi2 =  {res}  0.4003

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    0.57
{txt}{col 10}Prob > chi2 =  {res}  0.7529

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,228){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.3128{col 29}{space 2}0.3007{col 41}{space 3}0.2036{col 53}{space 3} 58.2813{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}58.2813     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estms_ivpw2 {col 30} estms_ols2 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .3439922{col 31} .7249929{col 47}-.3810007{col 63} .3287488
{txt}{space 6}med_hs {c |}{res}{col 18}-.1238576{col 31}-.1765479{col 47} .0526903{col 63} .0455064
{txt}{space 3}med_scoll {c |}{res}{col 18} .1984284{col 31} .0829267{col 47} .1155017{col 63} .1021613
{txt}med_collplus {c |}{res}{col 18} .0434692{col 31}-.1634776{col 47} .2069469{col 63} .2063969
{txt}{space 7}_cons {c |}{res}{col 18} 1.340372{col 31} .5185171{col 47} .8218552{col 63} .7094741
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:5})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      2.35
{txt}{col 17}Prob>chi2 =  {res}    0.7984



IV Specification 3



{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       233
{txt}{col 1}{col 51}Wald chi2({res}6{txt}){col 67}= {res}     26.88
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0002
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1249
{txt}{col 51}Root MSE{col 67}=    {res} 1.1631

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .1670307{col 27}{space 2} .3685461{col 38}{space 1}    0.45{col 47}{space 3}0.650{col 55}{space 4}-.5553063{col 68}{space 3} .8893677
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1677576{col 27}{space 2} .0449716{col 38}{space 1}   -3.73{col 47}{space 3}0.000{col 55}{space 4}-.2559003{col 68}{space 3}-.0796148
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2} .0336942{col 27}{space 2} .4631082{col 38}{space 1}    0.07{col 47}{space 3}0.942{col 55}{space 4}-.8739813{col 68}{space 3} .9413697
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .3650389{col 27}{space 2} .4702646{col 38}{space 1}    0.78{col 47}{space 3}0.438{col 55}{space 4}-.5566628{col 68}{space 3} 1.286741
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .2498153{col 27}{space 2} .5216763{col 38}{space 1}    0.48{col 47}{space 3}0.632{col 55}{space 4}-.7726513{col 68}{space 3} 1.272282
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0051034{col 27}{space 2} .0137865{col 38}{space 1}   -0.37{col 47}{space 3}0.711{col 55}{space 4}-.0321244{col 68}{space 3} .0219176
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.416271{col 27}{space 2} 1.037397{col 38}{space 1}    3.29{col 47}{space 3}0.001{col 55}{space 4}  1.38301{col 68}{space 3} 5.449531
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_hs med_scoll med_collplus m_age m_pw_inst{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.2005244{col 26}{space 2} .5311703{col 37}{space 1}   -0.38{col 46}{space 3}0.706{col 54}{space 4}-1.241599{col 67}{space 3} .8405502
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  3) ={res}    3.26
{txt}{col 10}Prob > chi2 =  {res}  0.3539

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    0.83
{txt}{col 10}Prob > chi2 =  {res}  0.6609

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,226){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.3273{col 29}{space 2}0.3094{col 41}{space 3}0.1934{col 53}{space 3} 54.1961{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}54.1961     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estms_ivpw3 {col 30} estms_ols3 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .1670307{col 31} .6503109{col 47}-.4832802{col 63} .3349497
{txt}{space 9}age {c |}{res}{col 18}-.1677576{col 31}-.1489885{col 47}-.0187691{col 63} .0155641
{txt}{space 6}med_hs {c |}{res}{col 18} .0336942{col 31} -.017764{col 47} .0514582{col 63} .0542221
{txt}{space 3}med_scoll {c |}{res}{col 18} .3650389{col 31} .2276683{col 47} .1373706{col 63} .1037651
{txt}med_collplus {c |}{res}{col 18} .2498153{col 31}-.0069247{col 47} .2567401{col 63} .2015337
{txt}{space 7}m_age {c |}{res}{col 18}-.0051034{col 31}-.0095501{col 47} .0044467{col 63} .0038504
{txt}{space 7}_cons {c |}{res}{col 18} 3.416271{col 31}  2.34194{col 47} 1.074331{col 63} .7524987
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:7})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      2.76
{txt}{col 17}Prob>chi2 =  {res}    0.9061



IV Specification 4



{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       233
{txt}{col 1}{col 51}Wald chi2({res}9{txt}){col 67}= {res}     32.04
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0002
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1550
{txt}{col 51}Root MSE{col 67}=    {res} 1.1429

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .2779107{col 27}{space 2} .3873231{col 38}{space 1}    0.72{col 47}{space 3}0.473{col 55}{space 4}-.4812285{col 68}{space 3}  1.03705
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1762453{col 27}{space 2} .0446377{col 38}{space 1}   -3.95{col 47}{space 3}0.000{col 55}{space 4}-.2637336{col 68}{space 3}-.0887569
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2} .0306503{col 27}{space 2} .4578662{col 38}{space 1}    0.07{col 47}{space 3}0.947{col 55}{space 4} -.866751{col 68}{space 3} .9280516
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .3151215{col 27}{space 2} .4688905{col 38}{space 1}    0.67{col 47}{space 3}0.502{col 55}{space 4}-.6038871{col 68}{space 3}  1.23413
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .2104191{col 27}{space 2} .5209426{col 38}{space 1}    0.40{col 47}{space 3}0.686{col 55}{space 4}-.8106096{col 68}{space 3} 1.231448
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0092863{col 27}{space 2} .0144983{col 38}{space 1}   -0.64{col 47}{space 3}0.522{col 55}{space 4}-.0377024{col 68}{space 3} .0191299
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.3649392{col 27}{space 2} .1747336{col 38}{space 1}   -2.09{col 47}{space 3}0.037{col 55}{space 4}-.7074107{col 68}{space 3}-.0224677
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.1229972{col 27}{space 2} .2409137{col 38}{space 1}   -0.51{col 47}{space 3}0.610{col 55}{space 4}-.5951793{col 68}{space 3} .3491849
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0284462{col 27}{space 2} .1166255{col 38}{space 1}    0.24{col 47}{space 3}0.807{col 55}{space 4}-.2001355{col 68}{space 3}  .257028
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.485666{col 27}{space 2} 1.121851{col 38}{space 1}    3.11{col 47}{space 3}0.002{col 55}{space 4} 1.286879{col 68}{space 3} 5.684453
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_hs med_scoll med_collplus m_age m_white num_0_5 num_child m_pw_inst{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.3848703{col 26}{space 2} .7428336{col 37}{space 1}   -0.52{col 46}{space 3}0.604{col 54}{space 4}-1.840797{col 67}{space 3} 1.071057
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  3) ={res}    2.37
{txt}{col 10}Prob > chi2 =  {res}  0.4988

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    0.65
{txt}{col 10}Prob > chi2 =  {res}  0.7209

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,223){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.3485{col 29}{space 2}0.3222{col 41}{space 3}0.1780{col 53}{space 3} 48.2755{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}48.2755     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estms_ivpw4 {col 30} estms_ols4 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .2779107{col 31} .7114233{col 47}-.4335126{col 63}  .354734
{txt}{space 9}age {c |}{res}{col 18}-.1762453{col 31}-.1620733{col 47} -.014172{col 63} .0121174
{txt}{space 6}med_hs {c |}{res}{col 18} .0306503{col 31}-.0079032{col 47} .0385535{col 63}        .
{txt}{space 3}med_scoll {c |}{res}{col 18} .3151215{col 31} .1909612{col 47} .1241603{col 63} .0931582
{txt}med_collplus {c |}{res}{col 18} .2104191{col 31} .0012957{col 47} .2091234{col 63} .1911305
{txt}{space 7}m_age {c |}{res}{col 18}-.0092863{col 31}-.0137145{col 47} .0044282{col 63} .0040341
{txt}{space 5}m_white {c |}{res}{col 18}-.3649392{col 31}-.4134099{col 47} .0484707{col 63} .0508862
{txt}{space 5}num_0_5 {c |}{res}{col 18}-.1229972{col 31}-.1386511{col 47} .0156539{col 63} .0114628
{txt}{space 3}num_child {c |}{res}{col 18} .0284462{col 31} .0805101{col 47}-.0520639{col 63}    .0398
{txt}{space 7}_cons {c |}{res}{col 18} 3.485666{col 31} 2.476783{col 47} 1.008883{col 63} .8159479
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:10})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      3.00
{txt}{col 17}Prob>chi2 =  {res}    0.9814
{txt}{col 17}(V_b-V_B is not positive definite)



IV Specification 5


{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       233
{txt}{col 1}{col 51}Wald chi2({res}8{txt}){col 67}= {res}     32.05
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0001
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1553
{txt}{col 51}Root MSE{col 67}=    {res} 1.1428

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .2805028{col 27}{space 2}   .38678{col 38}{space 1}    0.73{col 47}{space 3}0.468{col 55}{space 4}-.4775721{col 68}{space 3} 1.038578
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1760727{col 27}{space 2} .0445851{col 38}{space 1}   -3.95{col 47}{space 3}0.000{col 55}{space 4}-.2634579{col 68}{space 3}-.0886874
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .2861867{col 27}{space 2} .1891217{col 38}{space 1}    1.51{col 47}{space 3}0.130{col 55}{space 4} -.084485{col 68}{space 3} .6568585
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .1805077{col 27}{space 2} .2789889{col 38}{space 1}    0.65{col 47}{space 3}0.518{col 55}{space 4}-.3663004{col 68}{space 3} .7273159
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0091716{col 27}{space 2} .0143744{col 38}{space 1}   -0.64{col 47}{space 3}0.523{col 55}{space 4}-.0373449{col 68}{space 3} .0190017
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.3653032{col 27}{space 2} .1746782{col 38}{space 1}   -2.09{col 47}{space 3}0.037{col 55}{space 4}-.7076662{col 68}{space 3}-.0229402
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.1213462{col 27}{space 2} .2395673{col 38}{space 1}   -0.51{col 47}{space 3}0.612{col 55}{space 4}-.5908894{col 68}{space 3}  .348197
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0284367{col 27}{space 2} .1166051{col 38}{space 1}    0.24{col 47}{space 3}0.807{col 55}{space 4}-.2001051{col 68}{space 3} .2569785
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.502287{col 27}{space 2} 1.086079{col 38}{space 1}    3.22{col 47}{space 3}0.001{col 55}{space 4}  1.37361{col 68}{space 3} 5.630963
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_scoll med_collplus m_age m_white num_0_5 num_child m_pw_inst{p_end}
(option {bf:xb} assumed; fitted values)
(82 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.3898594{col 26}{space 2} .7471465{col 37}{space 1}   -0.52{col 46}{space 3}0.602{col 54}{space 4} -1.85424{col 67}{space 3} 1.074521
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    2.37
{txt}{col 10}Prob > chi2 =  {res}  0.3065

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,224){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.3483{col 29}{space 2}0.3250{col 41}{space 3}0.1783{col 53}{space 3} 48.5981{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}48.5981     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estms_ivpw5 {col 30} estms_ols5 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .2805028{col 31} .7113864{col 47}-.4308837{col 63} .3542978
{txt}{space 9}age {c |}{res}{col 18}-.1760727{col 31}-.1620978{col 47}-.0139749{col 63} .0123403
{txt}{space 3}med_scoll {c |}{res}{col 18} .2861867{col 31} .1982918{col 47} .0878949{col 63} .0757413
{txt}med_collplus {c |}{res}{col 18} .1805077{col 31} .0087094{col 47} .1717984{col 63} .1683692
{txt}{space 7}m_age {c |}{res}{col 18}-.0091716{col 31}-.0137502{col 47} .0045786{col 63} .0042231
{txt}{space 5}m_white {c |}{res}{col 18}-.3653032{col 31}-.4133996{col 47} .0480964{col 63} .0518987
{txt}{space 5}num_0_5 {c |}{res}{col 18}-.1213462{col 31}-.1390867{col 47} .0177405{col 63} .0193895
{txt}{space 3}num_child {c |}{res}{col 18} .0284367{col 31} .0805871{col 47}-.0521504{col 63} .0406461
{txt}{space 7}_cons {c |}{res}{col 18} 3.502287{col 31} 2.470977{col 47}  1.03131{col 63} .8381252
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:9})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      2.60
{txt}{col 17}Prob>chi2 =  {res}    0.9780
{txt}{col 17}(V_b-V_B is not positive definite)



IV Specification 6


{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       194
{txt}{col 1}{col 51}Wald chi2({res}9{txt}){col 67}= {res}     24.36
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0038
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1368
{txt}{col 51}Root MSE{col 67}=    {res} 1.1579

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .1876867{col 27}{space 2} .4774032{col 38}{space 1}    0.39{col 47}{space 3}0.694{col 55}{space 4}-.7480063{col 68}{space 3}  1.12338
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1985394{col 27}{space 2} .0513528{col 38}{space 1}   -3.87{col 47}{space 3}0.000{col 55}{space 4}-.2991891{col 68}{space 3}-.0978898
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .2354988{col 27}{space 2}  .226671{col 38}{space 1}    1.04{col 47}{space 3}0.299{col 55}{space 4}-.2087682{col 68}{space 3} .6797658
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .1603246{col 27}{space 2} .3474121{col 38}{space 1}    0.46{col 47}{space 3}0.644{col 55}{space 4}-.5205907{col 68}{space 3} .8412399
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0026716{col 27}{space 2} .0159893{col 38}{space 1}   -0.17{col 47}{space 3}0.867{col 55}{space 4}-.0340099{col 68}{space 3} .0286668
{txt}{space 6}m_white {c |}{col 15}{res}{space 2} .0126808{col 27}{space 2} .2095873{col 38}{space 1}    0.06{col 47}{space 3}0.952{col 55}{space 4}-.3981027{col 68}{space 3} .4234644
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.0097108{col 27}{space 2} .2599391{col 38}{space 1}   -0.04{col 47}{space 3}0.970{col 55}{space 4}-.5191821{col 68}{space 3} .4997606
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0687353{col 27}{space 2} .1260326{col 38}{space 1}    0.55{col 47}{space 3}0.585{col 55}{space 4} -.178284{col 68}{space 3} .3157546
{txt}{space 7}m_pc97 {c |}{col 15}{res}{space 2}-.0077461{col 27}{space 2} .0063095{col 38}{space 1}   -1.23{col 47}{space 3}0.220{col 55}{space 4}-.0201125{col 68}{space 3} .0046203
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 4.218884{col 27}{space 2} 1.286912{col 38}{space 1}    3.28{col 47}{space 3}0.001{col 55}{space 4} 1.696584{col 68}{space 3} 6.741185
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_scoll med_collplus m_age m_white num_0_5 num_child m_pc97 m_pw_inst{p_end}
(option {bf:xb} assumed; fitted values)
(146 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.2310522{col 26}{space 2} .7234995{col 37}{space 1}   -0.32{col 46}{space 3}0.749{col 54}{space 4}-1.649085{col 67}{space 3} 1.186981
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    1.15
{txt}{col 10}Prob > chi2 =  {res}  0.5618

{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,184){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.3760{col 29}{space 2}0.3454{col 41}{space 3}0.1514{col 53}{space 3} 32.8329{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}32.8329     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{p 0 8}Note: the rank of the differenced variance matrix ({result:9}) does not equal the number of coefficients being tested ({result:10}); be sure this is what you expect, or there may be problems computing the test.  Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale.

{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estms_ivpw6 {col 30} estms_ols6 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .1876867{col 31} .7398682{col 47}-.5521814{col 63} .4444545
{txt}{space 9}age {c |}{res}{col 18}-.1985394{col 31}-.1816478{col 47}-.0168916{col 63} .0167707
{txt}{space 3}med_scoll {c |}{res}{col 18} .2354988{col 31} .1128552{col 47} .1226436{col 63} .1074345
{txt}med_collplus {c |}{res}{col 18} .1603246{col 31}-.0838736{col 47} .2441982{col 63} .2318213
{txt}{space 7}m_age {c |}{res}{col 18}-.0026716{col 31}-.0088443{col 47} .0061727{col 63} .0053117
{txt}{space 5}m_white {c |}{res}{col 18} .0126808{col 31}-.0638549{col 47} .0765357{col 63} .0739755
{txt}{space 5}num_0_5 {c |}{res}{col 18}-.0097108{col 31}-.0296297{col 47} .0199189{col 63} .0302449
{txt}{space 3}num_child {c |}{res}{col 18} .0687353{col 31} .1202436{col 47}-.0515083{col 63} .0423745
{txt}{space 6}m_pc97 {c |}{res}{col 18}-.0077461{col 31} -.009492{col 47} .0017459{col 63} .0015223
{txt}{space 7}_cons {c |}{res}{col 18} 4.218884{col 31} 3.132998{col 47} 1.085886{col 63} .9300623
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:9})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      2.15
{txt}{col 17}Prob>chi2 =  {res}    0.9890
{txt}{col 17}(V_b-V_B is not positive definite)



IV Specification 7


{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       160
{txt}{col 1}{col 51}Wald chi2({res}6{txt}){col 67}= {res}     23.42
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0007
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1442
{txt}{col 51}Root MSE{col 67}=    {res} 1.1129

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .1213511{col 27}{space 2} .7005644{col 38}{space 1}    0.17{col 47}{space 3}0.862{col 55}{space 4} -1.25173{col 68}{space 3} 1.494432
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.184758{col 27}{space 2} .0574372{col 38}{space 1}   -3.22{col 47}{space 3}0.001{col 55}{space 4}-.2973329{col 68}{space 3}-.0721831
{txt}{space 2}ln_wageFE_m {c |}{col 15}{res}{space 2} .1788851{col 27}{space 2} .3666218{col 38}{space 1}    0.49{col 47}{space 3}0.626{col 55}{space 4}-.5396803{col 68}{space 3} .8974506
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.5270377{col 27}{space 2} .2099467{col 38}{space 1}   -2.51{col 47}{space 3}0.012{col 55}{space 4}-.9385257{col 68}{space 3}-.1155497
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .0063462{col 27}{space 2} .3629178{col 38}{space 1}    0.02{col 47}{space 3}0.986{col 55}{space 4}-.7049596{col 68}{space 3} .7176519
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}-.0785574{col 27}{space 2} .1354761{col 38}{space 1}   -0.58{col 47}{space 3}0.562{col 55}{space 4}-.3440858{col 68}{space 3} .1869709
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 4.015162{col 27}{space 2} 2.169657{col 38}{space 1}    1.85{col 47}{space 3}0.064{col 55}{space 4}-.2372867{col 68}{space 3} 8.267611
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age ln_wageFE_m m_white num_0_5 num_child m_pw_inst{p_end}
(option {bf:xb} assumed; fitted values)
(186 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} -.138111{col 26}{space 2} .9074388{col 37}{space 1}   -0.15{col 46}{space 3}0.879{col 54}{space 4}-1.916658{col 67}{space 3} 1.640436
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,153){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.3898{col 29}{space 2}0.3659{col 41}{space 3}0.0927{col 53}{space 3} 15.6231{col 65}{space 3}0.0001
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}15.6231     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}
{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estms_ivpw7 {col 30} estms_ols7 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .1213511{col 31} .7670501{col 47} -.645699{col 63} .6721465
{txt}{space 9}age {c |}{res}{col 18} -.184758{col 31}-.1630642{col 47}-.0216938{col 63} .0212434
{txt}{space 1}ln_wageFE_m {c |}{res}{col 18} .1788851{col 31}-.0888207{col 47} .2677058{col 63}  .308804
{txt}{space 5}m_white {c |}{res}{col 18}-.5270377{col 31}-.5794787{col 47}  .052441{col 63} .0846702
{txt}{space 5}num_0_5 {c |}{res}{col 18} .0063462{col 31}-.0106663{col 47} .0170125{col 63} .0457925
{txt}{space 3}num_child {c |}{res}{col 18}-.0785574{col 31}-.0105918{col 47}-.0679656{col 63} .0674602
{txt}{space 7}_cons {c |}{res}{col 18} 4.015162{col 31} 2.054826{col 47} 1.960336{col 63} 2.004979
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:7})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      1.71
{txt}{col 17}Prob>chi2 =  {res}    0.9742
{com}. 
.         
.         
. 
.         
. ***************** TWO-PARENT HOUSEHOLDS (2002, 2007) ****************
. 
. clear 
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. keep if curr_married==1
{txt}(556 observations deleted)
{com}. *Probability of work restriction > 0.75
. keep if prob_work_m >= `p_work_min_m' & prob_work_m !=.
{txt}(212 observations deleted)
{com}. 
. 
. ****************** Mother's time vs. goods **************************
. 
. * OLS estimates
. 
. forvalues zval=5/7 {c -(}
{txt}  2{com}.         disp _n(3) "OLS Specification " `zval' _n(2)
{txt}  3{com}.         regress ln_tau_m_g_exp_ratio  ln_wage_m_rel  `Zm`zval'' 
{txt}  4{com}.         predict PHImm_ols`zval'
{txt}  5{com}.         nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  6{com}.         capture noisily test `medvars'
{txt}  7{com}.         capture noisily test med_scoll med_collplus
{txt}  8{com}.         local rhomm_ols`zval' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
{txt}  9{com}.         capture noisily 
{txt} 10{com}.         estimates store estmm_ols`zval'
{txt} 11{com}. {c )-}



OLS Specification 5



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       491
{txt}{hline 13}{c +}{hline 34}   F(8, 482)       = {res}    14.47
{txt}       Model {c |} {res} 125.073579         8  15.6341974   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 520.860172       482  1.08062276   {txt}R-squared       ={res}    0.1936
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1802
{txt}       Total {c |} {res} 645.933751       490  1.31823214   {txt}Root MSE        =   {res} 1.0395

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2}  .627938{col 27}{space 2} .0794965{col 38}{space 1}    7.90{col 47}{space 3}0.000{col 55}{space 4} .4717355{col 68}{space 3} .7841405
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1316055{col 27}{space 2} .0260349{col 38}{space 1}   -5.05{col 47}{space 3}0.000{col 55}{space 4}-.1827614{col 68}{space 3}-.0804496
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1243583{col 27}{space 2}  .127726{col 38}{space 1}   -0.97{col 47}{space 3}0.331{col 55}{space 4} -.375327{col 68}{space 3} .1266103
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2687168{col 27}{space 2} .1320329{col 38}{space 1}   -2.04{col 47}{space 3}0.042{col 55}{space 4}-.5281479{col 68}{space 3}-.0092857
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0053975{col 27}{space 2} .0091094{col 38}{space 1}   -0.59{col 47}{space 3}0.554{col 55}{space 4}-.0232966{col 68}{space 3} .0125016
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1699007{col 27}{space 2} .1072055{col 38}{space 1}   -1.58{col 47}{space 3}0.114{col 55}{space 4}-.3805485{col 68}{space 3} .0407472
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2910007{col 27}{space 2} .1474027{col 38}{space 1}    1.97{col 47}{space 3}0.049{col 55}{space 4} .0013694{col 68}{space 3}  .580632
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1068818{col 27}{space 2} .0761342{col 38}{space 1}    1.40{col 47}{space 3}0.161{col 55}{space 4}-.0427141{col 68}{space 3} .2564778
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.981708{col 27}{space 2} .4289885{col 38}{space 1}    4.62{col 47}{space 3}0.000{col 55}{space 4}  1.13879{col 68}{space 3} 2.824627
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(146 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.687724{col 26}{space 2} .5742717{col 37}{space 1}   -2.94{col 46}{space 3}0.003{col 54}{space 4}-2.813276{col 67}{space 3}-.5621722
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   482) ={res}    2.10
{txt}{col 13}Prob > F ={res}    0.1232



OLS Specification 6



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       406
{txt}{hline 13}{c +}{hline 34}   F(9, 396)       = {res}     8.27
{txt}       Model {c |} {res} 79.8239671         9  8.86932968   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 424.656321       396  1.07236445   {txt}R-squared       ={res}    0.1582
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1391
{txt}       Total {c |} {res} 504.480288       405  1.24563034   {txt}Root MSE        =   {res} 1.0356

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .5642712{col 27}{space 2} .0870463{col 38}{space 1}    6.48{col 47}{space 3}0.000{col 55}{space 4} .3931407{col 68}{space 3} .7354018
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.129035{col 27}{space 2} .0287144{col 38}{space 1}   -4.49{col 47}{space 3}0.000{col 55}{space 4}-.1854867{col 68}{space 3}-.0725833
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1556289{col 27}{space 2} .1459133{col 38}{space 1}   -1.07{col 47}{space 3}0.287{col 55}{space 4}-.4424905{col 68}{space 3} .1312328
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.3469277{col 27}{space 2} .1543502{col 38}{space 1}   -2.25{col 47}{space 3}0.025{col 55}{space 4}-.6503759{col 68}{space 3}-.0434794
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} .0018987{col 27}{space 2}  .010128{col 38}{space 1}    0.19{col 47}{space 3}0.851{col 55}{space 4}-.0180127{col 68}{space 3} .0218102
{txt}{space 6}m_white {c |}{col 15}{res}{space 2} -.138619{col 27}{space 2} .1296801{col 38}{space 1}   -1.07{col 47}{space 3}0.286{col 55}{space 4}-.3935665{col 68}{space 3} .1163285
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1615609{col 27}{space 2} .1749682{col 38}{space 1}    0.92{col 47}{space 3}0.356{col 55}{space 4}-.1824219{col 68}{space 3} .5055437
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0920663{col 27}{space 2}  .084813{col 38}{space 1}    1.09{col 47}{space 3}0.278{col 55}{space 4}-.0746738{col 68}{space 3} .2588064
{txt}{space 7}m_pc97 {c |}{col 15}{res}{space 2}-.0029305{col 27}{space 2} .0039818{col 38}{space 1}   -0.74{col 47}{space 3}0.462{col 55}{space 4}-.0107585{col 68}{space 3} .0048976
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.182816{col 27}{space 2} .5344814{col 38}{space 1}    4.08{col 47}{space 3}0.000{col 55}{space 4} 1.132041{col 68}{space 3} 3.233592
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(265 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.295006{col 26}{space 2}  .458477{col 37}{space 1}   -2.82{col 46}{space 3}0.005{col 54}{space 4}-2.193604{col 67}{space 3}-.3964071
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   396) ={res}    2.63
{txt}{col 13}Prob > F ={res}    0.0732



OLS Specification 7



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       400
{txt}{hline 13}{c +}{hline 34}   F(6, 393)       = {res}    16.06
{txt}       Model {c |} {res} 101.735644         6  16.9559406   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 414.914465       393    1.055762   {txt}R-squared       ={res}    0.1969
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1847
{txt}       Total {c |} {res} 516.650108       399  1.29486243   {txt}Root MSE        =   {res} 1.0275

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7904741{col 27}{space 2} .1062055{col 38}{space 1}    7.44{col 47}{space 3}0.000{col 55}{space 4} .5816721{col 68}{space 3} .9992761
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1440246{col 27}{space 2}  .026906{col 38}{space 1}   -5.35{col 47}{space 3}0.000{col 55}{space 4}-.1969224{col 68}{space 3}-.0911269
{txt}{space 2}ln_wageFE_m {c |}{col 15}{res}{space 2}-.5031214{col 27}{space 2} .1406748{col 38}{space 1}   -3.58{col 47}{space 3}0.000{col 55}{space 4}-.7796906{col 68}{space 3}-.2265522
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2174814{col 27}{space 2} .1214324{col 38}{space 1}   -1.79{col 47}{space 3}0.074{col 55}{space 4}-.4562198{col 68}{space 3}  .021257
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2221792{col 27}{space 2} .1897525{col 38}{space 1}    1.17{col 47}{space 3}0.242{col 55}{space 4}-.1508776{col 68}{space 3} .5952361
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0464275{col 27}{space 2} .0823038{col 38}{space 1}    0.56{col 47}{space 3}0.573{col 55}{space 4}-.1153833{col 68}{space 3} .2082383
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.541985{col 27}{space 2} .4028901{col 38}{space 1}    3.83{col 47}{space 3}0.000{col 55}{space 4} .7498958{col 68}{space 3} 2.334075
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(262 missing values generated)

         rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} -3.77268{col 26}{space 2} 2.419199{col 37}{space 1}   -1.56{col 46}{space 3}0.119{col 54}{space 4}-8.514223{col 67}{space 3}  .968864
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found
{com}.         
. * IV estimates: state of residence
. 
. local zval= 5 
.         disp _n(3) "IV Specification " `zval' _n(2)
{res}


IV Specification 5


{com}.         ivregress 2sls ln_tau_m_g_exp_ratio `Zm`zval''  (ln_wage_m_rel=i.state i.year)
{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       491
{txt}{col 1}{col 51}Wald chi2({res}8{txt}){col 67}= {res}     65.30
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1904
{txt}{col 51}Root MSE{col 67}=    {res}  1.032

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7375905{col 27}{space 2} .2206973{col 38}{space 1}    3.34{col 47}{space 3}0.001{col 55}{space 4} .3050318{col 68}{space 3} 1.170149
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1310164{col 27}{space 2} .0258697{col 38}{space 1}   -5.06{col 47}{space 3}0.000{col 55}{space 4}-.1817202{col 68}{space 3}-.0803127
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1582656{col 27}{space 2} .1419153{col 38}{space 1}   -1.12{col 47}{space 3}0.265{col 55}{space 4}-.4364145{col 68}{space 3} .1198833
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.3369729{col 27}{space 2}  .183413{col 38}{space 1}   -1.84{col 47}{space 3}0.066{col 55}{space 4}-.6964557{col 68}{space 3} .0225099
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0066082{col 27}{space 2} .0093253{col 38}{space 1}   -0.71{col 47}{space 3}0.479{col 55}{space 4}-.0248854{col 68}{space 3}  .011669
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1733801{col 27}{space 2} .1066286{col 38}{space 1}   -1.63{col 47}{space 3}0.104{col 55}{space 4}-.3823683{col 68}{space 3} .0356081
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2860603{col 27}{space 2} .1466278{col 38}{space 1}    1.95{col 47}{space 3}0.051{col 55}{space 4}-.0013249{col 68}{space 3} .5734456
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1131105{col 27}{space 2} .0764833{col 38}{space 1}    1.48{col 47}{space 3}0.139{col 55}{space 4} -.036794{col 68}{space 3}  .263015
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.774329{col 27}{space 2} .5773295{col 38}{space 1}    3.07{col 47}{space 3}0.002{col 55}{space 4} .6427839{col 68}{space 3} 2.905874
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_scoll med_collplus m_age m_white num_0_5 num_child 4.state 5.state 6.state 8.state 9.state 12.state 13.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 30.state 31.state 32.state 34.state 36.state 37.state 38.state 39.state 40.state 41.state 42.state 45.state 46.state 47.state 48.state 49.state 51.state 53.state 55.state 2007.year{p_end}
{com}.         predict PHImm_ivs`zval'
{txt}(option {bf:xb} assumed; fitted values)
(146 missing values generated)
{com}.         nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.810838{col 26}{space 2} 3.205073{col 37}{space 1}   -0.88{col 46}{space 3}0.380{col 54}{space 4}-9.092665{col 67}{space 3} 3.470989
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}.         capture noisily test `medvars'
{bf}{err}med_hs{sf} not found
{com}.         capture noisily test med_scoll med_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    3.41
{txt}{col 10}Prob > chi2 =  {res}  0.1815
{com}.         local rhomm_ivs`zval' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
.         estimates store estmm_ivs`zval'
.         estat firststage
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(38,445){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.2865{col 29}{space 2}0.2143{col 41}{space 3}0.1279{col 53}{space 3} 1.71702{col 65}{space 3}0.0061
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}1.71702     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   38
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}{com}.         hausman estmm_ivs`zval' estmm_ols`zval', constant

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estmm_ivs5 {col 30} estmm_ols5 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .7375905{col 31}  .627938{col 47} .1096525{col 63} .2058825
{txt}{space 9}age {c |}{res}{col 18}-.1310164{col 31}-.1316055{col 47} .0005891{col 63}        .
{txt}{space 3}med_scoll {c |}{res}{col 18}-.1582656{col 31}-.1243583{col 47}-.0339072{col 63} .0618548
{txt}med_collplus {c |}{res}{col 18}-.3369729{col 31}-.2687168{col 47}-.0682561{col 63} .1273092
{txt}{space 7}m_age {c |}{res}{col 18}-.0066082{col 31}-.0053975{col 47}-.0012107{col 63} .0019948
{txt}{space 5}m_white {c |}{res}{col 18}-.1733801{col 31}-.1699007{col 47}-.0034794{col 63}        .
{txt}{space 5}num_0_5 {c |}{res}{col 18} .2860603{col 31} .2910007{col 47}-.0049403{col 63}        .
{txt}{space 3}num_child {c |}{res}{col 18} .1131105{col 31} .1068818{col 47} .0062287{col 63} .0072992
{txt}{space 7}_cons {c |}{res}{col 18} 1.774329{col 31} 1.981708{col 47}-.2073793{col 63} .3863654
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:9})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      0.28
{txt}{col 17}Prob>chi2 =  {res}    1.0000
{txt}{col 17}(V_b-V_B is not positive definite)
{com}.         local Hm`zval' = r(p)
. 
. 
. 
. * IV estimates: predicted wages
. 
. local zval= 5 
. 
.         disp _n(3) "IV Specification " `zval' _n(2)
{res}


IV Specification 5


{com}.         ivregress 2sls ln_tau_m_g_exp_ratio `Zm`zval''  (ln_wage_m_rel=m_pw_inst)
{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       487
{txt}{col 1}{col 51}Wald chi2({res}8{txt}){col 67}= {res}     62.44
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1945
{txt}{col 51}Root MSE{col 67}=    {res} 1.0294

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6968003{col 27}{space 2} .2277579{col 38}{space 1}    3.06{col 47}{space 3}0.002{col 55}{space 4}  .250403{col 68}{space 3} 1.143198
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1293921{col 27}{space 2} .0260243{col 38}{space 1}   -4.97{col 47}{space 3}0.000{col 55}{space 4}-.1803987{col 68}{space 3}-.0783854
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1561935{col 27}{space 2} .1423993{col 38}{space 1}   -1.10{col 47}{space 3}0.273{col 55}{space 4}-.4352909{col 68}{space 3}  .122904
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.3197614{col 27}{space 2}  .187564{col 38}{space 1}   -1.70{col 47}{space 3}0.088{col 55}{space 4}-.6873802{col 68}{space 3} .0478574
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0065342{col 27}{space 2}  .009323{col 38}{space 1}   -0.70{col 47}{space 3}0.483{col 55}{space 4}-.0248069{col 68}{space 3} .0117386
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1699277{col 27}{space 2} .1067087{col 38}{space 1}   -1.59{col 47}{space 3}0.111{col 55}{space 4}-.3790728{col 68}{space 3} .0392175
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2919192{col 27}{space 2} .1472984{col 38}{space 1}    1.98{col 47}{space 3}0.047{col 55}{space 4} .0032196{col 68}{space 3} .5806188
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1098828{col 27}{space 2} .0763681{col 38}{space 1}    1.44{col 47}{space 3}0.150{col 55}{space 4} -.039796{col 68}{space 3} .2595615
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.858112{col 27}{space 2}   .58247{col 38}{space 1}    3.19{col 47}{space 3}0.001{col 55}{space 4} .7164915{col 68}{space 3} 2.999732
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_m_rel{p_end}
{p 0 15 57}Instruments:{space 3}age med_scoll med_collplus m_age m_white num_0_5 num_child m_pw_inst{p_end}
{com}.         predict PHImm_ivpw`zval'
{txt}(option {bf:xb} assumed; fitted values)
(146 missing values generated)
{com}.         nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.298156{col 26}{space 2} 2.477512{col 37}{space 1}   -0.93{col 46}{space 3}0.354{col 54}{space 4}-7.153991{col 67}{space 3} 2.557679
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}.         capture noisily test `medvars'
{bf}{err}med_hs{sf} not found
{com}.         capture noisily test med_scoll med_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    2.92
{txt}{col 10}Prob > chi2 =  {res}  0.2326
{com}.         local rhomm_ivpw`zval' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
.         estimates store estmm_ivpw`zval'
.         estat firststage
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,478){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_m_~l{col 16}{c |}{result}{space 2}0.2846{col 29}{space 2}0.2726{col 41}{space 3}0.1225{col 53}{space 3} 66.7241{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}66.7241     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}{com}.         hausman estmm_ivpw`zval' estmm_ols`zval', constant

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}estmm_ivpw5 {col 30} estmm_ols5 {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_m_~l {c |}{res}{col 18} .6968003{col 31}  .627938{col 47} .0688623{col 63} .2134337
{txt}{space 9}age {c |}{res}{col 18}-.1293921{col 31}-.1316055{col 47} .0022134{col 63}        .
{txt}{space 3}med_scoll {c |}{res}{col 18}-.1561935{col 31}-.1243583{col 47}-.0318352{col 63} .0629572
{txt}med_collplus {c |}{res}{col 18}-.3197614{col 31}-.2687168{col 47}-.0510446{col 63} .1332201
{txt}{space 7}m_age {c |}{res}{col 18}-.0065342{col 31}-.0053975{col 47}-.0011367{col 63}  .001984
{txt}{space 5}m_white {c |}{res}{col 18}-.1699277{col 31}-.1699007{col 47} -.000027{col 63}        .
{txt}{space 5}num_0_5 {c |}{res}{col 18} .2919192{col 31} .2910007{col 47} .0009185{col 63}        .
{txt}{space 3}num_child {c |}{res}{col 18} .1098828{col 31} .1068818{col 47} .0030009{col 63} .0059726
{txt}{space 7}_cons {c |}{res}{col 18} 1.858112{col 31} 1.981708{col 47}-.1235964{col 63} .3940053
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:9})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      3.40
{txt}{col 17}Prob>chi2 =  {res}    0.9461
{txt}{col 17}(V_b-V_B is not positive definite)
{com}.         local Hmpw`zval' = r(p)
. 
. 
. 
. ***  Testing if father wages matter (which would indicate concerns about selection into work)
. 
. summ ln_wage_f,d

                          {txt}ln_wage_f
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.541001       1.306614
{txt} 5%    {res}  2.07545       1.306614
{txt}10%    {res} 2.263364       1.347074       {txt}Obs         {res}        662
{txt}25%    {res} 2.615118       1.359063       {txt}Sum of Wgt. {res}        662

{txt}50%    {res} 2.940371                      {txt}Mean          {res} 2.988584
                        {txt}Largest       Std. Dev.     {res} .6018434
{txt}75%    {res} 3.349116       4.677568
{txt}90%    {res} 3.795179       4.692182       {txt}Variance      {res} .3622155
{txt}95%    {res} 4.048796       4.909296       {txt}Skewness      {res} .2415181
{txt}99%    {res} 4.627332       4.909296       {txt}Kurtosis      {res} 3.328963
{com}. local med_ln_wage_f = r(p50)
. 
. 
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel `Zm5'       if ln_wage_f !=.

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       451
{txt}{hline 13}{c +}{hline 34}   F(8, 442)       = {res}    15.76
{txt}       Model {c |} {res} 130.437552         8   16.304694   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 457.346319       442  1.03472018   {txt}R-squared       ={res}    0.2219
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2078
{txt}       Total {c |} {res} 587.783871       450  1.30618638   {txt}Root MSE        =   {res} 1.0172

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7164012{col 27}{space 2} .0817149{col 38}{space 1}    8.77{col 47}{space 3}0.000{col 55}{space 4} .5558031{col 68}{space 3} .8769993
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.123985{col 27}{space 2} .0267839{col 38}{space 1}   -4.63{col 47}{space 3}0.000{col 55}{space 4}-.1766246{col 68}{space 3}-.0713454
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1449785{col 27}{space 2} .1327319{col 38}{space 1}   -1.09{col 47}{space 3}0.275{col 55}{space 4}-.4058427{col 68}{space 3} .1158856
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2983262{col 27}{space 2} .1362308{col 38}{space 1}   -2.19{col 47}{space 3}0.029{col 55}{space 4}-.5660668{col 68}{space 3}-.0305857
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0072724{col 27}{space 2}   .00953{col 38}{space 1}   -0.76{col 47}{space 3}0.446{col 55}{space 4}-.0260022{col 68}{space 3} .0114574
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1191513{col 27}{space 2} .1116259{col 38}{space 1}   -1.07{col 47}{space 3}0.286{col 55}{space 4}-.3385348{col 68}{space 3} .1002321
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}  .315113{col 27}{space 2} .1486075{col 38}{space 1}    2.12{col 47}{space 3}0.035{col 55}{space 4}  .023048{col 68}{space 3} .6071781
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0835728{col 27}{space 2}   .07957{col 38}{space 1}    1.05{col 47}{space 3}0.294{col 55}{space 4}-.0728096{col 68}{space 3} .2399553
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.779363{col 27}{space 2} .4427379{col 38}{space 1}    4.02{col 47}{space 3}0.000{col 55}{space 4} .9092298{col 68}{space 3} 2.649496
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. estimates store estm_ols_5
. local rssm_ols_5a=e(rss)
. local nm_ols_5a=e(N)
. local k2m_ols_5a=9
. 
. 
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel `Zm5'       if ln_wage_f <= `med_ln_wage_f' & ln_wage_f !=.

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       231
{txt}{hline 13}{c +}{hline 34}   F(8, 222)       = {res}     9.62
{txt}       Model {c |} {res} 75.5866177         8  9.44832721   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 217.931228       222    .9816722   {txt}R-squared       ={res}    0.2575
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2308
{txt}       Total {c |} {res} 293.517846       230  1.27616455   {txt}Root MSE        =   {res} .99079

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7972596{col 27}{space 2} .1107054{col 38}{space 1}    7.20{col 47}{space 3}0.000{col 55}{space 4} .5790917{col 68}{space 3} 1.015428
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0993272{col 27}{space 2} .0369934{col 38}{space 1}   -2.68{col 47}{space 3}0.008{col 55}{space 4}-.1722304{col 68}{space 3} -.026424
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.2934404{col 27}{space 2}  .166633{col 38}{space 1}   -1.76{col 47}{space 3}0.080{col 55}{space 4}-.6218252{col 68}{space 3} .0349444
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.3117235{col 27}{space 2}  .182173{col 38}{space 1}   -1.71{col 47}{space 3}0.088{col 55}{space 4}-.6707333{col 68}{space 3} .0472863
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0083911{col 27}{space 2} .0129698{col 38}{space 1}   -0.65{col 47}{space 3}0.518{col 55}{space 4}-.0339508{col 68}{space 3} .0171687
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1412283{col 27}{space 2} .1398226{col 38}{space 1}   -1.01{col 47}{space 3}0.314{col 55}{space 4}-.4167777{col 68}{space 3} .1343211
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .3728622{col 27}{space 2} .1891632{col 38}{space 1}    1.97{col 47}{space 3}0.050{col 55}{space 4}  .000077{col 68}{space 3} .7456474
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0717629{col 27}{space 2}  .107864{col 38}{space 1}    0.67{col 47}{space 3}0.507{col 55}{space 4}-.1408055{col 68}{space 3} .2843313
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.524986{col 27}{space 2}  .598609{col 38}{space 1}    2.55{col 47}{space 3}0.012{col 55}{space 4} .3453028{col 68}{space 3} 2.704669
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. estimates store estm_ols_5_lo
. local rssm_ols_5_lo=e(rss)
. 
. 
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel `Zm5'       if ln_wage_f > `med_ln_wage_f' & ln_wage_f !=.

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       220
{txt}{hline 13}{c +}{hline 34}   F(8, 211)       = {res}     6.73
{txt}       Model {c |} {res}  59.609277         8  7.45115963   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 233.663176       211  1.10740842   {txt}R-squared       ={res}    0.2033
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1730
{txt}       Total {c |} {res} 293.272453       219  1.33914362   {txt}Root MSE        =   {res} 1.0523

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6473221{col 27}{space 2} .1237364{col 38}{space 1}    5.23{col 47}{space 3}0.000{col 55}{space 4} .4034042{col 68}{space 3} .8912401
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1521527{col 27}{space 2} .0392089{col 38}{space 1}   -3.88{col 47}{space 3}0.000{col 55}{space 4}-.2294441{col 68}{space 3}-.0748613
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0870706{col 27}{space 2} .2207681{col 38}{space 1}    0.39{col 47}{space 3}0.694{col 55}{space 4}-.3481231{col 68}{space 3} .5222643
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} -.223547{col 27}{space 2} .2130757{col 38}{space 1}   -1.05{col 47}{space 3}0.295{col 55}{space 4}-.6435769{col 68}{space 3} .1964828
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} .0019176{col 27}{space 2} .0155974{col 38}{space 1}    0.12{col 47}{space 3}0.902{col 55}{space 4}-.0288291{col 68}{space 3} .0326643
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0310072{col 27}{space 2} .1935945{col 38}{space 1}   -0.16{col 47}{space 3}0.873{col 55}{space 4}-.4126343{col 68}{space 3} .3506199
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}  .271508{col 27}{space 2} .2412443{col 38}{space 1}    1.13{col 47}{space 3}0.262{col 55}{space 4}-.2040497{col 68}{space 3} .7470658
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1147962{col 27}{space 2} .1213713{col 38}{space 1}    0.95{col 47}{space 3}0.345{col 55}{space 4}-.1244594{col 68}{space 3} .3540518
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.576093{col 27}{space 2} .7630503{col 38}{space 1}    2.07{col 47}{space 3}0.040{col 55}{space 4} .0719144{col 68}{space 3} 3.080272
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. estimates store estm_ols_5_hi
. local rssm_ols_5_hi=e(rss)
. 
. *F-stat: test difference in coefficients (high/low father's wage)
. local Fm_ols_5a=(`nm_ols_5a'-2*`k2m_ols_5a')/`k2m_ols_5a'*(`rssm_ols_5a'-`rssm_ols_5_lo' -`rssm_ols_5_hi')/(`rssm_ols_5_lo' +`rssm_ols_5_hi')
. local pm_ols_5a=Ftail(`k2m_ols_5a',`nm_ols_5a'-2*`k2m_ols_5a',`Fm_ols_5a')
. local p: display %4.3f `pm_ols_5a'
{res}{com}. estadd scalar p_val_a= `pm_ols_5a' : estm_ols_5
. 
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-11: OLS estimates for mother time/goods relative demand by father's wage
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab  estm_ols_5 estm_ols_5_lo estm_ols_5_hi using "$tables/Mar_S1m_OLS_fwage.tex", coeflab(ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m,i{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children")  stats(r2 rss N , fmt(3 3 0))  ti(Appendix Table: OLS estimates for mother time/goods relative demand by father wage (MARRIED, 1-2 kids ages 0-12, P(work) $ \geq $ `p_work_min_m' 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(15) modelwidth(13) wrap style(tex) mtitles("All" "Below Median" "Above Median")  replace addnotes("p-value for (2) and (3) = `p'")
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/Mar_S1m_OLS_fwage.tex"'})
{com}. 
. 
. 
. ****  Some control function selection correction estimates **** 
. 
. clear 
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. keep if curr_married==1
{txt}(556 observations deleted)
{com}. 
. 
. disp _n(3) "HECKMAN TWO-STEP Estimates" _n(2)
{res}


HECKMAN TWO-STEP Estimates


{com}. 
. 
. heckman ln_tau_m_g_exp_ratio  ln_wage_m_rel `medvars' m_age `famvars' age i.year, select(pos_hrs_m = `medvars' m_age `famvars'  age i.year `fedvars' f_age) twostep
{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}       756
{txt}(regression model with sample selection)              Selected    = {res}       582
                                                {txt}      Nonselected = {res}       174

                                                {txt}Wald chi2({res}10{txt})     =  {res}   137.03
                                                {txt}Prob > chi2       =     {res}0.0000

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_tau_m_g_exp_ratio {txt}{c |}
{space 7}ln_wage_m_rel {c |}{col 22}{res}{space 2} .6440886{col 34}{space 2} .0751648{col 45}{space 1}    8.57{col 54}{space 3}0.000{col 62}{space 4} .4967682{col 75}{space 3} .7914089
{txt}{space 14}med_hs {c |}{col 22}{res}{space 2}-.1102388{col 34}{space 2}  .248307{col 45}{space 1}   -0.44{col 54}{space 3}0.657{col 62}{space 4}-.5969116{col 75}{space 3} .3764339
{txt}{space 11}med_scoll {c |}{col 22}{res}{space 2}-.3346606{col 34}{space 2} .2722889{col 45}{space 1}   -1.23{col 54}{space 3}0.219{col 62}{space 4} -.868337{col 75}{space 3} .1990158
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2}-.4718479{col 34}{space 2} .2955335{col 45}{space 1}   -1.60{col 54}{space 3}0.110{col 62}{space 4}-1.051083{col 75}{space 3}  .107387
{txt}{space 15}m_age {c |}{col 22}{res}{space 2}-.0011323{col 34}{space 2} .0091361{col 45}{space 1}   -0.12{col 54}{space 3}0.901{col 62}{space 4}-.0190387{col 75}{space 3}  .016774
{txt}{space 13}m_white {c |}{col 22}{res}{space 2}-.0731998{col 34}{space 2} .1112358{col 45}{space 1}   -0.66{col 54}{space 3}0.510{col 62}{space 4}-.2912181{col 75}{space 3} .1448184
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2} .3163069{col 34}{space 2} .1258128{col 45}{space 1}    2.51{col 54}{space 3}0.012{col 62}{space 4} .0697183{col 75}{space 3} .5628955
{txt}{space 11}num_child {c |}{col 22}{res}{space 2} .1048326{col 34}{space 2} .0696593{col 45}{space 1}    1.50{col 54}{space 3}0.132{col 62}{space 4}-.0316971{col 75}{space 3} .2413624
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.1338692{col 34}{space 2} .0275758{col 45}{space 1}   -4.85{col 54}{space 3}0.000{col 62}{space 4}-.1879168{col 75}{space 3}-.0798217
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2} .0899538{col 34}{space 2}  .133371{col 45}{space 1}    0.67{col 54}{space 3}0.500{col 62}{space 4}-.1714485{col 75}{space 3} .3513561
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2} 1.951577{col 34}{space 2} .4686642{col 45}{space 1}    4.16{col 54}{space 3}0.000{col 62}{space 4} 1.033012{col 75}{space 3} 2.870142
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_hrs_m            {txt}{c |}
{space 14}med_hs {c |}{col 22}{res}{space 2} .1778742{col 34}{space 2} .2341164{col 45}{space 1}    0.76{col 54}{space 3}0.447{col 62}{space 4}-.2809855{col 75}{space 3}  .636734
{txt}{space 11}med_scoll {c |}{col 22}{res}{space 2} .5028338{col 34}{space 2} .2478857{col 45}{space 1}    2.03{col 54}{space 3}0.043{col 62}{space 4} .0169868{col 75}{space 3} .9886808
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2} .7814864{col 34}{space 2} .2561305{col 45}{space 1}    3.05{col 54}{space 3}0.002{col 62}{space 4} .2794798{col 75}{space 3} 1.283493
{txt}{space 15}m_age {c |}{col 22}{res}{space 2}-.0137797{col 34}{space 2} .0150973{col 45}{space 1}   -0.91{col 54}{space 3}0.361{col 62}{space 4}-.0433698{col 75}{space 3} .0158105
{txt}{space 13}m_white {c |}{col 22}{res}{space 2}-.1853243{col 34}{space 2}  .128325{col 45}{space 1}   -1.44{col 54}{space 3}0.149{col 62}{space 4}-.4368368{col 75}{space 3} .0661881
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2}-.1179538{col 34}{space 2} .1390321{col 45}{space 1}   -0.85{col 54}{space 3}0.396{col 62}{space 4}-.3904518{col 75}{space 3} .1545442
{txt}{space 11}num_child {c |}{col 22}{res}{space 2}-.0407301{col 34}{space 2} .0794965{col 45}{space 1}   -0.51{col 54}{space 3}0.608{col 62}{space 4}-.1965404{col 75}{space 3} .1150802
{txt}{space 17}age {c |}{col 22}{res}{space 2} .0366557{col 34}{space 2} .0315969{col 45}{space 1}    1.16{col 54}{space 3}0.246{col 62}{space 4} -.025273{col 75}{space 3} .0985845
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2}-.2664261{col 34}{space 2} .1439041{col 45}{space 1}   -1.85{col 54}{space 3}0.064{col 62}{space 4}-.5484729{col 75}{space 3} .0156208
{txt}{space 20} {c |}
{space 14}fed_hs {c |}{col 22}{res}{space 2} .4330222{col 34}{space 2} .1914882{col 45}{space 1}    2.26{col 54}{space 3}0.024{col 62}{space 4} .0577122{col 75}{space 3} .8083322
{txt}{space 11}fed_scoll {c |}{col 22}{res}{space 2} .2279813{col 34}{space 2} .2090109{col 45}{space 1}    1.09{col 54}{space 3}0.275{col 62}{space 4}-.1816725{col 75}{space 3} .6376351
{txt}{space 8}fed_collplus {c |}{col 22}{res}{space 2}-.1740843{col 34}{space 2}   .21631{col 45}{space 1}   -0.80{col 54}{space 3}0.421{col 62}{space 4}-.5980441{col 75}{space 3} .2498756
{txt}{space 15}f_age {c |}{col 22}{res}{space 2}  .007312{col 34}{space 2} .0126085{col 45}{space 1}    0.58{col 54}{space 3}0.562{col 62}{space 4}-.0174001{col 75}{space 3} .0320242
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .3494233{col 34}{space 2} .5121776{col 45}{space 1}    0.68{col 54}{space 3}0.495{col 62}{space 4}-.6544263{col 75}{space 3} 1.353273
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills               {txt}{c |}
{space 14}lambda {c |}{col 22}{res}{space 2}-.1955125{col 34}{space 2} .4547307{col 45}{space 1}   -0.43{col 54}{space 3}0.667{col 62}{space 4}-1.086768{col 75}{space 3} .6957433
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 rho {c |} {res}  -0.18625
               {txt}sigma {c |} {res} 1.0497368
{txt}{hline 21}{c BT}{hline 64}
{com}. estimates store heck1
. 
. heckman ln_tau_m_g_exp_ratio  ln_wage_m_rel med_scoll med_collplus m_age `famvars' age i.year, select(pos_hrs_m = med_scoll med_collplus m_age `famvars'  age i.year `fedvars' f_age) twostep
{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}       756
{txt}(regression model with sample selection)              Selected    = {res}       582
                                                {txt}      Nonselected = {res}       174

                                                {txt}Wald chi2({res}9{txt})      =  {res}   137.08
                                                {txt}Prob > chi2       =     {res}0.0000

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_tau_m_g_exp_ratio {txt}{c |}
{space 7}ln_wage_m_rel {c |}{col 22}{res}{space 2} .6400839{col 34}{space 2} .0745382{col 45}{space 1}    8.59{col 54}{space 3}0.000{col 62}{space 4} .4939917{col 75}{space 3} .7861761
{txt}{space 11}med_scoll {c |}{col 22}{res}{space 2}  -.22952{col 34}{space 2}  .126782{col 45}{space 1}   -1.81{col 54}{space 3}0.070{col 62}{space 4}-.4780082{col 75}{space 3} .0189682
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2}-.3606802{col 34}{space 2} .1473876{col 45}{space 1}   -2.45{col 54}{space 3}0.014{col 62}{space 4}-.6495546{col 75}{space 3}-.0718059
{txt}{space 15}m_age {c |}{col 22}{res}{space 2}-.0016032{col 34}{space 2}  .009039{col 45}{space 1}   -0.18{col 54}{space 3}0.859{col 62}{space 4}-.0193192{col 75}{space 3} .0161128
{txt}{space 13}m_white {c |}{col 22}{res}{space 2}-.0835522{col 34}{space 2} .1080001{col 45}{space 1}   -0.77{col 54}{space 3}0.439{col 62}{space 4}-.2952284{col 75}{space 3}  .128124
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2} .3112528{col 34}{space 2} .1249058{col 45}{space 1}    2.49{col 54}{space 3}0.013{col 62}{space 4}  .066442{col 75}{space 3} .5560637
{txt}{space 11}num_child {c |}{col 22}{res}{space 2} .1065096{col 34}{space 2} .0694897{col 45}{space 1}    1.53{col 54}{space 3}0.125{col 62}{space 4}-.0296877{col 75}{space 3}  .242707
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.1333782{col 34}{space 2} .0274639{col 45}{space 1}   -4.86{col 54}{space 3}0.000{col 62}{space 4}-.1872064{col 75}{space 3}  -.07955
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2} .0857455{col 34}{space 2} .1329169{col 45}{space 1}    0.65{col 54}{space 3}0.519{col 62}{space 4}-.1747669{col 75}{space 3} .3462579
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2} 1.852537{col 34}{space 2} .4088598{col 45}{space 1}    4.53{col 54}{space 3}0.000{col 62}{space 4} 1.051187{col 75}{space 3} 2.653888
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_hrs_m            {txt}{c |}
{space 11}med_scoll {c |}{col 22}{res}{space 2} .3474386{col 34}{space 2} .1403665{col 45}{space 1}    2.48{col 54}{space 3}0.013{col 62}{space 4} .0723254{col 75}{space 3} .6225518
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2} .6251475{col 34}{space 2} .1528188{col 45}{space 1}    4.09{col 54}{space 3}0.000{col 62}{space 4} .3256282{col 75}{space 3} .9246667
{txt}{space 15}m_age {c |}{col 22}{res}{space 2}-.0132352{col 34}{space 2} .0150601{col 45}{space 1}   -0.88{col 54}{space 3}0.379{col 62}{space 4}-.0427525{col 75}{space 3} .0162821
{txt}{space 13}m_white {c |}{col 22}{res}{space 2} -.173177{col 34}{space 2} .1271398{col 45}{space 1}   -1.36{col 54}{space 3}0.173{col 62}{space 4}-.4223665{col 75}{space 3} .0760124
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2}-.1117916{col 34}{space 2} .1387095{col 45}{space 1}   -0.81{col 54}{space 3}0.420{col 62}{space 4}-.3836572{col 75}{space 3}  .160074
{txt}{space 11}num_child {c |}{col 22}{res}{space 2}-.0484271{col 34}{space 2} .0787523{col 45}{space 1}   -0.61{col 54}{space 3}0.539{col 62}{space 4}-.2027788{col 75}{space 3} .1059246
{txt}{space 17}age {c |}{col 22}{res}{space 2} .0377491{col 34}{space 2} .0315593{col 45}{space 1}    1.20{col 54}{space 3}0.232{col 62}{space 4} -.024106{col 75}{space 3} .0996042
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2}-.2753272{col 34}{space 2} .1433165{col 45}{space 1}   -1.92{col 54}{space 3}0.055{col 62}{space 4}-.5562225{col 75}{space 3}  .005568
{txt}{space 20} {c |}
{space 14}fed_hs {c |}{col 22}{res}{space 2} .4782076{col 34}{space 2} .1817925{col 45}{space 1}    2.63{col 54}{space 3}0.009{col 62}{space 4} .1219009{col 75}{space 3} .8345142
{txt}{space 11}fed_scoll {c |}{col 22}{res}{space 2}  .269239{col 34}{space 2} .2015946{col 45}{space 1}    1.34{col 54}{space 3}0.182{col 62}{space 4} -.125879{col 75}{space 3} .6643571
{txt}{space 8}fed_collplus {c |}{col 22}{res}{space 2}-.1367813{col 34}{space 2} .2104928{col 45}{space 1}   -0.65{col 54}{space 3}0.516{col 62}{space 4}-.5493396{col 75}{space 3}  .275777
{txt}{space 15}f_age {c |}{col 22}{res}{space 2} .0068701{col 34}{space 2} .0125795{col 45}{space 1}    0.55{col 54}{space 3}0.585{col 62}{space 4}-.0177852{col 75}{space 3} .0315255
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .4595574{col 34}{space 2} .4914217{col 45}{space 1}    0.94{col 54}{space 3}0.350{col 62}{space 4}-.5036114{col 75}{space 3} 1.422726
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills               {txt}{c |}
{space 14}lambda {c |}{col 22}{res}{space 2}-.1308172{col 34}{space 2} .4279275{col 45}{space 1}   -0.31{col 54}{space 3}0.760{col 62}{space 4}-.9695397{col 75}{space 3} .7079053
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 rho {c |} {res}  -0.12510
               {txt}sigma {c |} {res} 1.0456736
{txt}{hline 21}{c BT}{hline 64}
{com}. estimates store heck2
. 
. 
. heckman ln_tau_m_g_exp_ratio  ln_wage_m_rel med_scoll med_collplus m_age `famvars' age i.year, select(pos_hrs_m = med_scoll med_collplus m_age `famvars' age i.year ln_pchcare_4ca) twostep
{txt}note: two-step estimate of rho = -1.2736577 is being truncated to -1
{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}       771
{txt}(regression model with sample selection)              Selected    = {res}       593
                                                {txt}      Nonselected = {res}       178

                                                {txt}Wald chi2({res}9{txt})      =  {res}    38.89
                                                {txt}Prob > chi2       =     {res}0.0000

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_tau_m_g_exp_ratio {txt}{c |}
{space 7}ln_wage_m_rel {c |}{col 22}{res}{space 2} .6400997{col 34}{space 2} .1284423{col 45}{space 1}    4.98{col 54}{space 3}0.000{col 62}{space 4} .3883574{col 75}{space 3}  .891842
{txt}{space 11}med_scoll {c |}{col 22}{res}{space 2}-.5585876{col 34}{space 2} .7914036{col 45}{space 1}   -0.71{col 54}{space 3}0.480{col 62}{space 4} -2.10971{col 75}{space 3} .9925349
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2}-.8459998{col 34}{space 2} 1.186962{col 45}{space 1}   -0.71{col 54}{space 3}0.476{col 62}{space 4}-3.172402{col 75}{space 3} 1.480402
{txt}{space 15}m_age {c |}{col 22}{res}{space 2} .0161567{col 34}{space 2} .0480787{col 45}{space 1}    0.34{col 54}{space 3}0.737{col 62}{space 4}-.0780759{col 75}{space 3} .1103893
{txt}{space 13}m_white {c |}{col 22}{res}{space 2} .1713418{col 34}{space 2} .6509576{col 45}{space 1}    0.26{col 54}{space 3}0.792{col 62}{space 4}-1.104512{col 75}{space 3} 1.447195
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2} .4692151{col 34}{space 2} .4667264{col 45}{space 1}    1.01{col 54}{space 3}0.315{col 62}{space 4}-.4455518{col 75}{space 3} 1.383982
{txt}{space 11}num_child {c |}{col 22}{res}{space 2}  .175904{col 34}{space 2} .2128645{col 45}{space 1}    0.83{col 54}{space 3}0.409{col 62}{space 4}-.2413028{col 75}{space 3} .5931107
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.1708616{col 34}{space 2} .1119319{col 45}{space 1}   -1.53{col 54}{space 3}0.127{col 62}{space 4}-.3902441{col 75}{space 3} .0485209
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2} .3360844{col 34}{space 2} .6055295{col 45}{space 1}    0.56{col 54}{space 3}0.579{col 62}{space 4}-.8507316{col 75}{space 3} 1.522901
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2} 2.287325{col 34}{space 2} 1.408869{col 45}{space 1}    1.62{col 54}{space 3}0.104{col 62}{space 4}-.4740066{col 75}{space 3} 5.048657
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_hrs_m            {txt}{c |}
{space 11}med_scoll {c |}{col 22}{res}{space 2} .2870021{col 34}{space 2} .1256109{col 45}{space 1}    2.28{col 54}{space 3}0.022{col 62}{space 4} .0408092{col 75}{space 3}  .533195
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2} .4530452{col 34}{space 2} .1263736{col 45}{space 1}    3.58{col 54}{space 3}0.000{col 62}{space 4} .2053574{col 75}{space 3}  .700733
{txt}{space 15}m_age {c |}{col 22}{res}{space 2}-.0188112{col 34}{space 2} .0097658{col 45}{space 1}   -1.93{col 54}{space 3}0.054{col 62}{space 4}-.0379518{col 75}{space 3} .0003294
{txt}{space 13}m_white {c |}{col 22}{res}{space 2}-.2680228{col 34}{space 2} .1247272{col 45}{space 1}   -2.15{col 54}{space 3}0.032{col 62}{space 4}-.5124836{col 75}{space 3}-.0235621
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2}-.1540236{col 34}{space 2} .1354401{col 45}{space 1}   -1.14{col 54}{space 3}0.255{col 62}{space 4}-.4194813{col 75}{space 3} .1114342
{txt}{space 11}num_child {c |}{col 22}{res}{space 2}-.0606021{col 34}{space 2}  .077059{col 45}{space 1}   -0.79{col 54}{space 3}0.432{col 62}{space 4}-.2116351{col 75}{space 3} .0904308
{txt}{space 17}age {c |}{col 22}{res}{space 2} .0393363{col 34}{space 2} .0307184{col 45}{space 1}    1.28{col 54}{space 3}0.200{col 62}{space 4}-.0208707{col 75}{space 3} .0995433
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2}-.2190241{col 34}{space 2} .1404138{col 45}{space 1}   -1.56{col 54}{space 3}0.119{col 62}{space 4}-.4942302{col 75}{space 3} .0561819
{txt}{space 20} {c |}
{space 6}ln_pchcare_4ca {c |}{col 22}{res}{space 2} .0815901{col 34}{space 2} .1577655{col 45}{space 1}    0.52{col 54}{space 3}0.605{col 62}{space 4}-.2276246{col 75}{space 3} .3908048
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 1.167528{col 34}{space 2} .4584728{col 45}{space 1}    2.55{col 54}{space 3}0.011{col 62}{space 4}  .268938{col 75}{space 3} 2.066118
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills               {txt}{c |}
{space 14}lambda {c |}{col 22}{res}{space 2}-2.429692{col 34}{space 2} 5.697154{col 45}{space 1}   -0.43{col 54}{space 3}0.670{col 62}{space 4}-13.59591{col 75}{space 3} 8.736525
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 rho {c |} {res}  -1.00000
               {txt}sigma {c |} {res} 2.4296918
{txt}{hline 21}{c BT}{hline 64}
{com}. estimates store heck3c
. 
. heckman ln_tau_m_g_exp_ratio  ln_wage_m_rel med_scoll med_collplus m_age `famvars' age i.year, select(pos_hrs_m = med_scoll med_collplus m_age `famvars' age i.year `fedvars' f_age ln_pchcare_4ca) twostep
{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}       756
{txt}(regression model with sample selection)              Selected    = {res}       582
                                                {txt}      Nonselected = {res}       174

                                                {txt}Wald chi2({res}9{txt})      =  {res}   137.00
                                                {txt}Prob > chi2       =     {res}0.0000

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_tau_m_g_exp_ratio {txt}{c |}
{space 7}ln_wage_m_rel {c |}{col 22}{res}{space 2} .6398301{col 34}{space 2} .0745141{col 45}{space 1}    8.59{col 54}{space 3}0.000{col 62}{space 4} .4937852{col 75}{space 3} .7858751
{txt}{space 11}med_scoll {c |}{col 22}{res}{space 2}-.2370969{col 34}{space 2} .1263849{col 45}{space 1}   -1.88{col 54}{space 3}0.061{col 62}{space 4}-.4848067{col 75}{space 3} .0106129
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2}-.3714516{col 34}{space 2} .1458726{col 45}{space 1}   -2.55{col 54}{space 3}0.011{col 62}{space 4}-.6573566{col 75}{space 3}-.0855466
{txt}{space 15}m_age {c |}{col 22}{res}{space 2}-.0011882{col 34}{space 2} .0090049{col 45}{space 1}   -0.13{col 54}{space 3}0.895{col 62}{space 4}-.0188375{col 75}{space 3}  .016461
{txt}{space 13}m_white {c |}{col 22}{res}{space 2} -.078577{col 34}{space 2} .1074575{col 45}{space 1}   -0.73{col 54}{space 3}0.465{col 62}{space 4}-.2891898{col 75}{space 3} .1320358
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2} .3151442{col 34}{space 2} .1250566{col 45}{space 1}    2.52{col 54}{space 3}0.012{col 62}{space 4} .0700378{col 75}{space 3} .5602506
{txt}{space 11}num_child {c |}{col 22}{res}{space 2} .1080719{col 34}{space 2} .0694906{col 45}{space 1}    1.56{col 54}{space 3}0.120{col 62}{space 4}-.0281272{col 75}{space 3}  .244271
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.1344169{col 34}{space 2} .0274355{col 45}{space 1}   -4.90{col 54}{space 3}0.000{col 62}{space 4}-.1881895{col 75}{space 3}-.0806443
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2} .0919423{col 34}{space 2}   .13259{col 45}{space 1}    0.69{col 54}{space 3}0.488{col 62}{space 4}-.1679292{col 75}{space 3} .3518138
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2} 1.865339{col 34}{space 2} .4103165{col 45}{space 1}    4.55{col 54}{space 3}0.000{col 62}{space 4} 1.061133{col 75}{space 3} 2.669544
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_hrs_m            {txt}{c |}
{space 11}med_scoll {c |}{col 22}{res}{space 2}  .343111{col 34}{space 2} .1405791{col 45}{space 1}    2.44{col 54}{space 3}0.015{col 62}{space 4}  .067581{col 75}{space 3} .6186409
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2} .6193573{col 34}{space 2} .1529891{col 45}{space 1}    4.05{col 54}{space 3}0.000{col 62}{space 4} .3195042{col 75}{space 3} .9192104
{txt}{space 15}m_age {c |}{col 22}{res}{space 2}  -.01508{col 34}{space 2} .0151868{col 45}{space 1}   -0.99{col 54}{space 3}0.321{col 62}{space 4}-.0448455{col 75}{space 3} .0146855
{txt}{space 13}m_white {c |}{col 22}{res}{space 2}-.1918696{col 34}{space 2} .1284778{col 45}{space 1}   -1.49{col 54}{space 3}0.135{col 62}{space 4}-.4436814{col 75}{space 3} .0599423
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2}-.1158543{col 34}{space 2} .1388231{col 45}{space 1}   -0.83{col 54}{space 3}0.404{col 62}{space 4}-.3879426{col 75}{space 3} .1562339
{txt}{space 11}num_child {c |}{col 22}{res}{space 2}-.0472677{col 34}{space 2} .0789208{col 45}{space 1}   -0.60{col 54}{space 3}0.549{col 62}{space 4}-.2019496{col 75}{space 3} .1074143
{txt}{space 17}age {c |}{col 22}{res}{space 2} .0391434{col 34}{space 2} .0316455{col 45}{space 1}    1.24{col 54}{space 3}0.216{col 62}{space 4}-.0228806{col 75}{space 3} .1011675
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2}-.2881873{col 34}{space 2} .1441515{col 45}{space 1}   -2.00{col 54}{space 3}0.046{col 62}{space 4}-.5707191{col 75}{space 3}-.0056556
{txt}{space 20} {c |}
{space 14}fed_hs {c |}{col 22}{res}{space 2} .4941378{col 34}{space 2} .1826746{col 45}{space 1}    2.71{col 54}{space 3}0.007{col 62}{space 4} .1361021{col 75}{space 3} .8521735
{txt}{space 11}fed_scoll {c |}{col 22}{res}{space 2} .2756388{col 34}{space 2} .2017997{col 45}{space 1}    1.37{col 54}{space 3}0.172{col 62}{space 4}-.1198814{col 75}{space 3}  .671159
{txt}{space 8}fed_collplus {c |}{col 22}{res}{space 2}-.1353288{col 34}{space 2} .2105104{col 45}{space 1}   -0.64{col 54}{space 3}0.520{col 62}{space 4}-.5479216{col 75}{space 3} .2772639
{txt}{space 15}f_age {c |}{col 22}{res}{space 2} .0071853{col 34}{space 2} .0125957{col 45}{space 1}    0.57{col 54}{space 3}0.568{col 62}{space 4}-.0175018{col 75}{space 3} .0318724
{txt}{space 6}ln_pchcare_4ca {c |}{col 22}{res}{space 2} .1610901{col 34}{space 2}  .162333{col 45}{space 1}    0.99{col 54}{space 3}0.321{col 62}{space 4}-.1570768{col 75}{space 3}  .479257
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .3302128{col 34}{space 2} .5091393{col 45}{space 1}    0.65{col 54}{space 3}0.517{col 62}{space 4}-.6676818{col 75}{space 3} 1.328107
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills               {txt}{c |}
{space 14}lambda {c |}{col 22}{res}{space 2}-.1863556{col 34}{space 2}  .419535{col 45}{space 1}   -0.44{col 54}{space 3}0.657{col 62}{space 4}-1.008629{col 75}{space 3}  .635918
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 rho {c |} {res}  -0.17763
               {txt}sigma {c |} {res} 1.0491036
{txt}{hline 21}{c BT}{hline 64}
{com}. estimates store heck4c
. 
. heckman ln_tau_m_g_exp_ratio  ln_wage_m_rel med_scoll med_collplus m_age `famvars' age i.year if prob_work_m >= `p_work_min_m' & prob_work_m !=., select(pos_hrs_m = med_scoll med_collplus m_age `famvars' age i.year `fedvars' f_age ln_pchcare_4ca) twostep
{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}       610
{txt}(regression model with sample selection)              Selected    = {res}       491
                                                {txt}      Nonselected = {res}       119

                                                {txt}Wald chi2({res}9{txt})      =  {res}   115.79
                                                {txt}Prob > chi2       =     {res}0.0000

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_tau_m_g_exp_ratio {txt}{c |}
{space 7}ln_wage_m_rel {c |}{col 22}{res}{space 2} .6333691{col 34}{space 2} .0791791{col 45}{space 1}    8.00{col 54}{space 3}0.000{col 62}{space 4}  .478181{col 75}{space 3} .7885573
{txt}{space 11}med_scoll {c |}{col 22}{res}{space 2}-.1478666{col 34}{space 2} .1360158{col 45}{space 1}   -1.09{col 54}{space 3}0.277{col 62}{space 4}-.4144527{col 75}{space 3} .1187194
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2}-.3023998{col 34}{space 2} .1433723{col 45}{space 1}   -2.11{col 54}{space 3}0.035{col 62}{space 4}-.5834042{col 75}{space 3}-.0213953
{txt}{space 15}m_age {c |}{col 22}{res}{space 2}-.0037139{col 34}{space 2} .0096058{col 45}{space 1}   -0.39{col 54}{space 3}0.699{col 62}{space 4}-.0225409{col 75}{space 3} .0151131
{txt}{space 13}m_white {c |}{col 22}{res}{space 2}-.1538056{col 34}{space 2} .1117016{col 45}{space 1}   -1.38{col 54}{space 3}0.169{col 62}{space 4}-.3727367{col 75}{space 3} .0651255
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2} .2829098{col 34}{space 2} .1489123{col 45}{space 1}    1.90{col 54}{space 3}0.057{col 62}{space 4}-.0089528{col 75}{space 3} .5747725
{txt}{space 11}num_child {c |}{col 22}{res}{space 2} .1160627{col 34}{space 2} .0769783{col 45}{space 1}    1.51{col 54}{space 3}0.132{col 62}{space 4} -.034812{col 75}{space 3} .2669374
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.1417005{col 34}{space 2} .0298732{col 45}{space 1}   -4.74{col 54}{space 3}0.000{col 62}{space 4}-.2002508{col 75}{space 3}-.0831502
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2} .0841455{col 34}{space 2} .1393146{col 45}{space 1}    0.60{col 54}{space 3}0.546{col 62}{space 4}-.1889061{col 75}{space 3} .3571971
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2} 2.035848{col 34}{space 2}  .434953{col 45}{space 1}    4.68{col 54}{space 3}0.000{col 62}{space 4} 1.183356{col 75}{space 3} 2.888341
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_hrs_m            {txt}{c |}
{space 11}med_scoll {c |}{col 22}{res}{space 2} .4244536{col 34}{space 2} .1678842{col 45}{space 1}    2.53{col 54}{space 3}0.011{col 62}{space 4} .0954067{col 75}{space 3} .7535006
{txt}{space 8}med_collplus {c |}{col 22}{res}{space 2} .6686848{col 34}{space 2} .1846937{col 45}{space 1}    3.62{col 54}{space 3}0.000{col 62}{space 4} .3066919{col 75}{space 3} 1.030678
{txt}{space 15}m_age {c |}{col 22}{res}{space 2}-.0177445{col 34}{space 2} .0181093{col 45}{space 1}   -0.98{col 54}{space 3}0.327{col 62}{space 4} -.053238{col 75}{space 3}  .017749
{txt}{space 13}m_white {c |}{col 22}{res}{space 2} -.156562{col 34}{space 2} .1532701{col 45}{space 1}   -1.02{col 54}{space 3}0.307{col 62}{space 4}-.4569659{col 75}{space 3} .1438418
{txt}{space 13}num_0_5 {c |}{col 22}{res}{space 2} .1704228{col 34}{space 2} .2116116{col 45}{space 1}    0.81{col 54}{space 3}0.421{col 62}{space 4}-.2443284{col 75}{space 3}  .585174
{txt}{space 11}num_child {c |}{col 22}{res}{space 2}-.0733459{col 34}{space 2} .0908407{col 45}{space 1}   -0.81{col 54}{space 3}0.419{col 62}{space 4}-.2513903{col 75}{space 3} .1046985
{txt}{space 17}age {c |}{col 22}{res}{space 2} .0526661{col 34}{space 2} .0378762{col 45}{space 1}    1.39{col 54}{space 3}0.164{col 62}{space 4}-.0215699{col 75}{space 3} .1269022
{txt}{space 20} {c |}
{space 16}year {c |}
{space 15}2007  {c |}{col 22}{res}{space 2}-.2638393{col 34}{space 2} .1745533{col 45}{space 1}   -1.51{col 54}{space 3}0.131{col 62}{space 4}-.6059574{col 75}{space 3} .0782789
{txt}{space 20} {c |}
{space 14}fed_hs {c |}{col 22}{res}{space 2} .6163232{col 34}{space 2} .3882579{col 45}{space 1}    1.59{col 54}{space 3}0.112{col 62}{space 4}-.1446483{col 75}{space 3} 1.377295
{txt}{space 11}fed_scoll {c |}{col 22}{res}{space 2}  .338324{col 34}{space 2} .3972719{col 45}{space 1}    0.85{col 54}{space 3}0.394{col 62}{space 4}-.4403147{col 75}{space 3} 1.116963
{txt}{space 8}fed_collplus {c |}{col 22}{res}{space 2}-.1975944{col 34}{space 2} .4108689{col 45}{space 1}   -0.48{col 54}{space 3}0.631{col 62}{space 4}-1.002883{col 75}{space 3} .6076937
{txt}{space 15}f_age {c |}{col 22}{res}{space 2} .0133488{col 34}{space 2} .0153363{col 45}{space 1}    0.87{col 54}{space 3}0.384{col 62}{space 4}-.0167098{col 75}{space 3} .0434073
{txt}{space 6}ln_pchcare_4ca {c |}{col 22}{res}{space 2} .1164486{col 34}{space 2} .1900208{col 45}{space 1}    0.61{col 54}{space 3}0.540{col 62}{space 4}-.2559853{col 75}{space 3} .4888825
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .0432495{col 34}{space 2} .6601513{col 45}{space 1}    0.07{col 54}{space 3}0.948{col 62}{space 4}-1.250623{col 75}{space 3} 1.337122
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills               {txt}{c |}
{space 14}lambda {c |}{col 22}{res}{space 2}-.1829531{col 34}{space 2} .4272217{col 45}{space 1}   -0.43{col 54}{space 3}0.668{col 62}{space 4}-1.020292{col 75}{space 3} .6543861
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 rho {c |} {res}  -0.17665
               {txt}sigma {c |} {res} 1.0356774
{txt}{hline 21}{c BT}{hline 64}
{com}. estimates store heck5c
. 
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-9: Heckman two-step estimates for mother time/goods relative demand (two-parent households)
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab  heck1 heck2 heck3c heck4c heck5c using "$tables/Mar_S1m_Heckman.tex", coeflab(ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}m,i{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children")  stats(N N_selected, fmt(0 0))  ti(Appendix Table: Heckman two-step estimates for mother time/goods relative demand (MARRIED, 1-2 kids ages 0-12, 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(20) modelwidth(30) wrap style(tex) mtitles("father only" "father only"  "P_c only"  "Both" "Both, P(work) $ \geq $ `p_work_min_m'")  replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/Mar_S1m_Heckman.tex"'})
{com}. 
. 
. 
. ************* Father's time vs. goods  **********************
. 
. clear 
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. keep if curr_married==1
{txt}(556 observations deleted)
{com}. *Probability of work restriction > 0.9
. keep if prob_work_f >= `p_work_min_f' & prob_work_f !=.
{txt}(76 observations deleted)
{com}. 
. 
. 
. * OLS estimates
. 
. forvalues zval=5/7 {c -(}
{txt}  2{com}.         disp _n(3) "OLS Specification " `zval' _n(2)
{txt}  3{com}.         regress ln_tau_f_g_exp_ratio  ln_wage_f_rel  `Zf`zval'' 
{txt}  4{com}.         predict PHIf_ols`zval'
{txt}  5{com}.         nlcom rho: _b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)
{txt}  6{com}.         capture noisily test `fedvars'
{txt}  7{com}.         capture noisily test fed_scoll fed_collplus
{txt}  8{com}.         local rhof_ols`zval' = _b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)
{txt}  9{com}.         capture noisily 
{txt} 10{com}.         estimates store estf_ols`zval'
{txt} 11{com}. {c )-}



OLS Specification 5



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       582
{txt}{hline 13}{c +}{hline 34}   F(8, 573)       = {res}    13.00
{txt}       Model {c |} {res} 139.839212         8  17.4799015   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 770.302151       573  1.34433185   {txt}R-squared       ={res}    0.1536
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1418
{txt}       Total {c |} {res} 910.141364       581  1.56650837   {txt}Root MSE        =   {res} 1.1595

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_f_rel {c |}{col 15}{res}{space 2} .6778651{col 27}{space 2} .0896724{col 38}{space 1}    7.56{col 47}{space 3}0.000{col 55}{space 4} .5017385{col 68}{space 3} .8539917
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1073358{col 27}{space 2} .0265679{col 38}{space 1}   -4.04{col 47}{space 3}0.000{col 55}{space 4}-.1595182{col 68}{space 3}-.0551533
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2}-.1295592{col 27}{space 2} .1305496{col 38}{space 1}   -0.99{col 47}{space 3}0.321{col 55}{space 4}-.3859733{col 68}{space 3} .1268549
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2} .0710678{col 27}{space 2} .1267438{col 38}{space 1}    0.56{col 47}{space 3}0.575{col 55}{space 4}-.1778713{col 68}{space 3}  .320007
{txt}{space 8}f_age {c |}{col 15}{res}{space 2}-.0100924{col 27}{space 2} .0081044{col 38}{space 1}   -1.25{col 47}{space 3}0.214{col 55}{space 4}-.0260104{col 68}{space 3} .0058255
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0527072{col 27}{space 2} .1234486{col 38}{space 1}   -0.43{col 47}{space 3}0.670{col 55}{space 4}-.2951742{col 68}{space 3} .1897597
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1477398{col 27}{space 2} .1340448{col 38}{space 1}    1.10{col 47}{space 3}0.271{col 55}{space 4}-.1155393{col 68}{space 3} .4110189
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1682977{col 27}{space 2} .0795296{col 38}{space 1}    2.12{col 47}{space 3}0.035{col 55}{space 4} .0120926{col 68}{space 3} .3245029
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.281674{col 27}{space 2} .4343194{col 38}{space 1}    2.95{col 47}{space 3}0.003{col 55}{space 4} .4286219{col 68}{space 3} 2.134726
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(86 missing values generated)

         rho:  {res}_b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} -2.10429{col 26}{space 2}  .864138{col 37}{space 1}   -2.44{col 46}{space 3}0.015{col 54}{space 4}-3.797969{col 67}{space 3}-.4106104
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}fed_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_collplus = 0{p_end}

{txt}       F(  2,   573) ={res}    1.11
{txt}{col 13}Prob > F ={res}    0.3294



OLS Specification 6



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       582
{txt}{hline 13}{c +}{hline 34}   F(8, 573)       = {res}    13.00
{txt}       Model {c |} {res} 139.839212         8  17.4799015   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 770.302151       573  1.34433185   {txt}R-squared       ={res}    0.1536
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1418
{txt}       Total {c |} {res} 910.141364       581  1.56650837   {txt}Root MSE        =   {res} 1.1595

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_f_rel {c |}{col 15}{res}{space 2} .6778651{col 27}{space 2} .0896724{col 38}{space 1}    7.56{col 47}{space 3}0.000{col 55}{space 4} .5017385{col 68}{space 3} .8539917
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1073358{col 27}{space 2} .0265679{col 38}{space 1}   -4.04{col 47}{space 3}0.000{col 55}{space 4}-.1595182{col 68}{space 3}-.0551533
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2}-.1295592{col 27}{space 2} .1305496{col 38}{space 1}   -0.99{col 47}{space 3}0.321{col 55}{space 4}-.3859733{col 68}{space 3} .1268549
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2} .0710678{col 27}{space 2} .1267438{col 38}{space 1}    0.56{col 47}{space 3}0.575{col 55}{space 4}-.1778713{col 68}{space 3}  .320007
{txt}{space 8}f_age {c |}{col 15}{res}{space 2}-.0100924{col 27}{space 2} .0081044{col 38}{space 1}   -1.25{col 47}{space 3}0.214{col 55}{space 4}-.0260104{col 68}{space 3} .0058255
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0527072{col 27}{space 2} .1234486{col 38}{space 1}   -0.43{col 47}{space 3}0.670{col 55}{space 4}-.2951742{col 68}{space 3} .1897597
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1477398{col 27}{space 2} .1340448{col 38}{space 1}    1.10{col 47}{space 3}0.271{col 55}{space 4}-.1155393{col 68}{space 3} .4110189
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1682977{col 27}{space 2} .0795296{col 38}{space 1}    2.12{col 47}{space 3}0.035{col 55}{space 4} .0120926{col 68}{space 3} .3245029
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.281674{col 27}{space 2} .4343194{col 38}{space 1}    2.95{col 47}{space 3}0.003{col 55}{space 4} .4286219{col 68}{space 3} 2.134726
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(86 missing values generated)

         rho:  {res}_b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} -2.10429{col 26}{space 2}  .864138{col 37}{space 1}   -2.44{col 46}{space 3}0.015{col 54}{space 4}-3.797969{col 67}{space 3}-.4106104
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}fed_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_collplus = 0{p_end}

{txt}       F(  2,   573) ={res}    1.11
{txt}{col 13}Prob > F ={res}    0.3294



OLS Specification 7



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       413
{txt}{hline 13}{c +}{hline 34}   F(6, 406)       = {res}    13.95
{txt}       Model {c |} {res} 105.777466         6  17.6295777   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 512.983692       406  1.26350663   {txt}R-squared       ={res}    0.1710
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1587
{txt}       Total {c |} {res} 618.761158       412  1.50184747   {txt}Root MSE        =   {res} 1.1241

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_f_rel {c |}{col 15}{res}{space 2} .7790087{col 27}{space 2} .1214629{col 38}{space 1}    6.41{col 47}{space 3}0.000{col 55}{space 4}  .540234{col 68}{space 3} 1.017783
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.126596{col 27}{space 2} .0300154{col 38}{space 1}   -4.22{col 47}{space 3}0.000{col 55}{space 4} -.185601{col 68}{space 3} -.067591
{txt}{space 2}ln_wageFE_f {c |}{col 15}{res}{space 2}-.1712624{col 27}{space 2} .1224439{col 38}{space 1}   -1.40{col 47}{space 3}0.163{col 55}{space 4}-.4119656{col 68}{space 3} .0694408
{txt}{space 6}m_white {c |}{col 15}{res}{space 2} -.287455{col 27}{space 2} .1550403{col 38}{space 1}   -1.85{col 47}{space 3}0.064{col 55}{space 4} -.592237{col 68}{space 3}  .017327
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .3032726{col 27}{space 2} .1865448{col 38}{space 1}    1.63{col 47}{space 3}0.105{col 55}{space 4}-.0634416{col 68}{space 3} .6699868
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1685284{col 27}{space 2} .0920803{col 38}{space 1}    1.83{col 47}{space 3}0.068{col 55}{space 4}-.0124854{col 68}{space 3} .3495421
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .9593493{col 27}{space 2} .4822646{col 38}{space 1}    1.99{col 47}{space 3}0.047{col 55}{space 4} .0113019{col 68}{space 3} 1.907397
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(option {bf:xb} assumed; fitted values)
(357 missing values generated)

         rho:  {res}_b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-3.525065{col 26}{space 2}   2.4871{col 37}{space 1}   -1.42{col 46}{space 3}0.156{col 54}{space 4}-8.399691{col 67}{space 3} 1.349562
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}fed_hs{sf} not found
{bf}fed_scoll{sf} not found
{com}.         
.         
. * IV estimates: state of residence
. 
. local zval=5 
.         disp _n(3) "IV Specification " `zval' _n(2)
{res}


IV Specification 5


{com}.         ivregress 2sls ln_tau_f_g_exp_ratio `Zf`zval''  (ln_wage_f_rel=i.state i.year)
{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       582
{txt}{col 1}{col 51}Wald chi2({res}8{txt}){col 67}= {res}     50.14
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1475
{txt}{col 51}Root MSE{col 67}=    {res} 1.1546

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_f_rel {c |}{col 15}{res}{space 2} .4954294{col 27}{space 2} .2928166{col 38}{space 1}    1.69{col 47}{space 3}0.091{col 55}{space 4}-.0784805{col 68}{space 3} 1.069339
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1057338{col 27}{space 2} .0265698{col 38}{space 1}   -3.98{col 47}{space 3}0.000{col 55}{space 4}-.1578098{col 68}{space 3}-.0536579
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2}-.0690342{col 27}{space 2} .1595631{col 38}{space 1}   -0.43{col 47}{space 3}0.665{col 55}{space 4} -.381772{col 68}{space 3} .2437037
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2} .1709546{col 27}{space 2} .1980976{col 38}{space 1}    0.86{col 47}{space 3}0.388{col 55}{space 4}-.2173096{col 68}{space 3} .5592187
{txt}{space 8}f_age {c |}{col 15}{res}{space 2}-.0088717{col 27}{space 2} .0082834{col 38}{space 1}   -1.07{col 47}{space 3}0.284{col 55}{space 4}-.0251068{col 68}{space 3} .0073635
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0311911{col 27}{space 2} .1272556{col 38}{space 1}   -0.25{col 47}{space 3}0.806{col 55}{space 4}-.2806074{col 68}{space 3} .2182252
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1464034{col 27}{space 2} .1334995{col 38}{space 1}    1.10{col 47}{space 3}0.273{col 55}{space 4}-.1152508{col 68}{space 3} .4080575
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1751462{col 27}{space 2} .0798857{col 38}{space 1}    2.19{col 47}{space 3}0.028{col 55}{space 4} .0185732{col 68}{space 3} .3317193
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.678942{col 27}{space 2} .7455331{col 38}{space 1}    2.25{col 47}{space 3}0.024{col 55}{space 4} .2177239{col 68}{space 3}  3.14016
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_f_rel{p_end}
{p 0 15 57}Instruments:{space 3}age fed_scoll fed_collplus f_age m_white num_0_5 num_child 4.state 5.state 6.state 8.state 9.state 10.state 12.state 13.state 17.state 18.state 19.state 21.state 22.state 24.state 25.state 26.state 27.state 28.state 29.state 30.state 31.state 32.state 33.state 34.state 36.state 37.state 38.state 39.state 40.state 41.state 42.state 44.state 45.state 46.state 47.state 48.state 49.state 51.state 53.state 54.state 55.state 2007.year{p_end}
{com}.         predict PHIf_ivs`zval'
{txt}(option {bf:xb} assumed; fitted values)
(86 missing values generated)
{com}.         nlcom rho: _b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.9818834{col 26}{space 2} 1.150143{col 37}{space 1}   -0.85{col 46}{space 3}0.393{col 54}{space 4}-3.236122{col 67}{space 3} 1.272356
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}.         capture noisily test `fedvars'
{bf}{err}fed_hs{sf} not found
{com}.         capture noisily test fed_scoll fed_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    2.65
{txt}{col 10}Prob > chi2 =  {res}  0.2661
{com}.         local rhof_ivs`zval' = _b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)
.         estimates store estf_ivs`zval'
.         estat firststage
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}  F(42,532){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_f_~l{col 16}{c |}{result}{space 2}0.2799{col 29}{space 2}0.2136{col 41}{space 3}0.0930{col 53}{space 3} 1.29879{col 65}{space 3}0.1039
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}1.29879     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}   42
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c BT}{hline 33}

{res}{com}.         hausman estf_ivs`zval' estf_ols`zval', constant

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estf_ivs5  {col 30} estf_ols5  {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_f_~l {c |}{res}{col 18} .4954294{col 31} .6778651{col 47}-.1824357{col 63} .2787479
{txt}{space 9}age {c |}{res}{col 18}-.1057338{col 31}-.1073358{col 47}  .001602{col 63} .0003176
{txt}{space 3}fed_scoll {c |}{res}{col 18}-.0690342{col 31}-.1295592{col 47} .0605251{col 63} .0917452
{txt}fed_collplus {c |}{res}{col 18} .1709546{col 31} .0710678{col 47} .0998868{col 63} .1522454
{txt}{space 7}f_age {c |}{res}{col 18}-.0088717{col 31}-.0100924{col 47} .0012208{col 63} .0017128
{txt}{space 5}m_white {c |}{res}{col 18}-.0311911{col 31}-.0527072{col 47} .0215161{col 63} .0308937
{txt}{space 5}num_0_5 {c |}{res}{col 18} .1464034{col 31} .1477398{col 47}-.0013365{col 63}        .
{txt}{space 3}num_child {c |}{res}{col 18} .1751462{col 31} .1682977{col 47} .0068485{col 63} .0075342
{txt}{space 7}_cons {c |}{res}{col 18} 1.678942{col 31} 1.281674{col 47} .3972679{col 63}  .605959
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:9})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      0.43
{txt}{col 17}Prob>chi2 =  {res}    1.0000
{txt}{col 17}(V_b-V_B is not positive definite)
{com}.         local Hf`zval' = r(p)
. 
. 
. 
. * IV estimates: predicted wages
. 
. local zval=5 
.         disp _n(3) "IV Specification " `zval' _n(2)
{res}


IV Specification 5


{com}.         ivregress 2sls ln_tau_f_g_exp_ratio `Zf`zval''  (ln_wage_f_rel=f_pw_inst)
{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}       578
{txt}{col 1}{col 51}Wald chi2({res}8{txt}){col 67}= {res}     49.38
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.1361
{txt}{col 51}Root MSE{col 67}=    {res} 1.1637

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_f_rel {c |}{col 15}{res}{space 2} .3457477{col 27}{space 2} .2571182{col 38}{space 1}    1.34{col 47}{space 3}0.179{col 55}{space 4}-.1581947{col 68}{space 3} .8496902
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0992855{col 27}{space 2} .0270541{col 38}{space 1}   -3.67{col 47}{space 3}0.000{col 55}{space 4}-.1523105{col 68}{space 3}-.0462604
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2}-.0201392{col 27}{space 2} .1541133{col 38}{space 1}   -0.13{col 47}{space 3}0.896{col 55}{space 4}-.3221956{col 68}{space 3} .2819172
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2} .2755865{col 27}{space 2} .1833569{col 38}{space 1}    1.50{col 47}{space 3}0.133{col 55}{space 4}-.0837864{col 68}{space 3} .6349594
{txt}{space 8}f_age {c |}{col 15}{res}{space 2}-.0103959{col 27}{space 2} .0085823{col 38}{space 1}   -1.21{col 47}{space 3}0.226{col 55}{space 4}-.0272169{col 68}{space 3} .0064251
{txt}{space 6}m_white {c |}{col 15}{res}{space 2} .0007538{col 27}{space 2} .1277556{col 38}{space 1}    0.01{col 47}{space 3}0.995{col 55}{space 4}-.2496426{col 68}{space 3} .2511502
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1537694{col 27}{space 2} .1347579{col 38}{space 1}    1.14{col 47}{space 3}0.254{col 55}{space 4}-.1103512{col 68}{space 3} .4178901
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1821585{col 27}{space 2} .0806926{col 38}{space 1}    2.26{col 47}{space 3}0.024{col 55}{space 4} .0240039{col 68}{space 3} .3403131
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.028114{col 27}{space 2} .6825229{col 38}{space 1}    2.97{col 47}{space 3}0.003{col 55}{space 4} .6903939{col 68}{space 3} 3.365835
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 57}Instrumented:{space 2}ln_wage_f_rel{p_end}
{p 0 15 57}Instruments:{space 3}age fed_scoll fed_collplus f_age m_white num_0_5 num_child f_pw_inst{p_end}
{com}.         predict PHIf_ivpw`zval'
{txt}(option {bf:xb} assumed; fitted values)
(86 missing values generated)
{com}.         nlcom rho: _b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-.5284624{col 26}{space 2} .6006789{col 37}{space 1}   -0.88{col 46}{space 3}0.379{col 54}{space 4}-1.705771{col 67}{space 3} .6488466
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}.         capture noisily test `fedvars'
{bf}{err}fed_hs{sf} not found
{com}.         capture noisily test fed_scoll fed_collplus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_collplus = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    4.26
{txt}{col 10}Prob > chi2 =  {res}  0.1191
{com}.         local rhof_ivpw`zval' = _b[ln_wage_f_rel]/(_b[ln_wage_f_rel] -1.0)
.         estimates store estf_ivpw`zval'
.         estat firststage
{res}
{txt}{col 3}First-stage regression summary statistics
{res}{col 3}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 16}{c |}{col 29}Adjusted{col 43}Partial
{col 3}{text}    Variable{col 16}{c |}   R-sq.{col 29}   R-sq.{col 41}    R-sq.{col 53}   F(1,569){col 65}  Prob > F
{res}{col 3}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}
{col 3}{text}ln_wage_f_~l{col 16}{c |}{result}{space 2}0.3024{col 29}{space 2}0.2926{col 41}{space 3}0.1227{col 53}{space 3} 79.6051{col 65}{space 3}0.0000
{col 3}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 12}{hline 12}{hline 12}


{col 3}Minimum eigenvalue statistic = {res}79.6051     

{txt}{col 3}Critical Values{col 40}# of endogenous regressors:{res}    1
{txt}{col 3}Ho: Instruments are weak{col 40}# of excluded instruments: {res}    1
{txt}{col 3}{hline 35}{c TT}{hline 33}
{col 38}{c |}{col 43}5%     10%     20%     30%
{col 3}2SLS relative bias{col 38}{c |}{col 48}(not available)
{col 3}{hline 35}{c +}{hline 33}
{col 38}{c |}{col 42}10%     15%     20%     25%
{col 3}2SLS Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}LIML Size of nominal 5% Wald test{col 38}{c |}{res}{col 41}16.38{col 49} 8.96{col 57} 6.66{col 65} 5.53
{txt}{col 3}{hline 35}{c BT}{hline 33}

{res}{com}.         hausman estf_ivpw`zval' estf_ols`zval', constant

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17} estf_ivpw5 {col 30} estf_ols5  {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
ln_wage_f_~l {c |}{res}{col 18} .3457477{col 31} .6778651{col 47}-.3321174{col 63} .2409744
{txt}{space 9}age {c |}{res}{col 18}-.0992855{col 31}-.1073358{col 47} .0080503{col 63} .0051057
{txt}{space 3}fed_scoll {c |}{res}{col 18}-.0201392{col 31}-.1295592{col 47}   .10942{col 63} .0819006
{txt}fed_collplus {c |}{res}{col 18} .2755865{col 31} .0710678{col 47} .2045187{col 63} .1324981
{txt}{space 7}f_age {c |}{res}{col 18}-.0103959{col 31}-.0100924{col 47}-.0003034{col 63}  .002824
{txt}{space 5}m_white {c |}{res}{col 18} .0007538{col 31}-.0527072{col 47}  .053461{col 63} .0328929
{txt}{space 5}num_0_5 {c |}{res}{col 18} .1537694{col 31} .1477398{col 47} .0060296{col 63} .0138449
{txt}{space 3}num_child {c |}{res}{col 18} .1821585{col 31} .1682977{col 47} .0138607{col 63} .0136505
{txt}{space 7}_cons {c |}{res}{col 18} 2.028114{col 31} 1.281674{col 47} .7464402{col 63} .5265019
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from ivregress}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from regress}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:9})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}      5.06
{txt}{col 17}Prob>chi2 =  {res}    0.8290
{com}.         local Hfpw`zval' = r(p)
. 
. 
. ******* Tests of equality of columns 2-4, Table 3 *******
. clear 
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. 
. ****Tests for Table 3
. *Table 3 regressions 2-4
. regress ln_tau_m_g_exp_ratio ln_wage_m_rel `Zm5' if (curr_married==0 & prob_work_m >= `p_work_min_m' & prob_work_m !=.)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       236
{txt}{hline 13}{c +}{hline 34}   F(8, 227)       = {res}     6.98
{txt}       Model {c |} {res} 73.2717766         8  9.15897207   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 297.946123       227  1.31253799   {txt}R-squared       ={res}    0.1974
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1691
{txt}       Total {c |} {res} 371.217899       235  1.57965064   {txt}Root MSE        =   {res} 1.1457

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7113864{col 27}{space 2} .1551512{col 38}{space 1}    4.59{col 47}{space 3}0.000{col 55}{space 4} .4056658{col 68}{space 3} 1.017107
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1620978{col 27}{space 2} .0428433{col 38}{space 1}   -3.78{col 47}{space 3}0.000{col 55}{space 4}-.2465193{col 68}{space 3}-.0776763
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1982918{col 27}{space 2} .1732925{col 38}{space 1}    1.14{col 47}{space 3}0.254{col 55}{space 4}-.1431757{col 68}{space 3} .5397594
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .0087094{col 27}{space 2} .2224559{col 38}{space 1}    0.04{col 47}{space 3}0.969{col 55}{space 4}-.4296331{col 68}{space 3} .4470519
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0137502{col 27}{space 2}   .01374{col 38}{space 1}   -1.00{col 47}{space 3}0.318{col 55}{space 4}-.0408246{col 68}{space 3} .0133241
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.4133996{col 27}{space 2} .1667903{col 38}{space 1}   -2.48{col 47}{space 3}0.014{col 55}{space 4}-.7420548{col 68}{space 3}-.0847444
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.1390867{col 27}{space 2} .2387813{col 38}{space 1}   -0.58{col 47}{space 3}0.561{col 55}{space 4} -.609598{col 68}{space 3} .3314246
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0805871{col 27}{space 2} .1092916{col 38}{space 1}    0.74{col 47}{space 3}0.462{col 55}{space 4}-.1347686{col 68}{space 3} .2959428
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.470977{col 27}{space 2} .6907348{col 38}{space 1}    3.58{col 47}{space 3}0.000{col 55}{space 4} 1.109905{col 68}{space 3} 3.832049
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. estimates store est_t3_col2
. 
. regress ln_tau_m_g_exp_ratio ln_wage_m_rel `Zm5' if (curr_married==1 & prob_work_m >= `p_work_min_m' & prob_work_m !=.)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       491
{txt}{hline 13}{c +}{hline 34}   F(8, 482)       = {res}    14.47
{txt}       Model {c |} {res} 125.073579         8  15.6341974   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 520.860172       482  1.08062276   {txt}R-squared       ={res}    0.1936
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1802
{txt}       Total {c |} {res} 645.933751       490  1.31823214   {txt}Root MSE        =   {res} 1.0395

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2}  .627938{col 27}{space 2} .0794965{col 38}{space 1}    7.90{col 47}{space 3}0.000{col 55}{space 4} .4717355{col 68}{space 3} .7841405
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1316055{col 27}{space 2} .0260349{col 38}{space 1}   -5.05{col 47}{space 3}0.000{col 55}{space 4}-.1827614{col 68}{space 3}-.0804496
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1243583{col 27}{space 2}  .127726{col 38}{space 1}   -0.97{col 47}{space 3}0.331{col 55}{space 4} -.375327{col 68}{space 3} .1266103
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2687168{col 27}{space 2} .1320329{col 38}{space 1}   -2.04{col 47}{space 3}0.042{col 55}{space 4}-.5281479{col 68}{space 3}-.0092857
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0053975{col 27}{space 2} .0091094{col 38}{space 1}   -0.59{col 47}{space 3}0.554{col 55}{space 4}-.0232966{col 68}{space 3} .0125016
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1699007{col 27}{space 2} .1072055{col 38}{space 1}   -1.58{col 47}{space 3}0.114{col 55}{space 4}-.3805485{col 68}{space 3} .0407472
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2910007{col 27}{space 2} .1474027{col 38}{space 1}    1.97{col 47}{space 3}0.049{col 55}{space 4} .0013694{col 68}{space 3}  .580632
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1068818{col 27}{space 2} .0761342{col 38}{space 1}    1.40{col 47}{space 3}0.161{col 55}{space 4}-.0427141{col 68}{space 3} .2564778
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.981708{col 27}{space 2} .4289885{col 38}{space 1}    4.62{col 47}{space 3}0.000{col 55}{space 4}  1.13879{col 68}{space 3} 2.824627
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. estimates store est_t3_col3
. 
. regress ln_tau_f_g_exp_ratio ln_wage_f_rel `Zf5' if (curr_married==1 & prob_work_f >= `p_work_min_f' & prob_work_f !=.)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       582
{txt}{hline 13}{c +}{hline 34}   F(8, 573)       = {res}    13.00
{txt}       Model {c |} {res} 139.839212         8  17.4799015   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 770.302151       573  1.34433185   {txt}R-squared       ={res}    0.1536
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1418
{txt}       Total {c |} {res} 910.141364       581  1.56650837   {txt}Root MSE        =   {res} 1.1595

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_f_rel {c |}{col 15}{res}{space 2} .6778651{col 27}{space 2} .0896724{col 38}{space 1}    7.56{col 47}{space 3}0.000{col 55}{space 4} .5017385{col 68}{space 3} .8539917
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1073358{col 27}{space 2} .0265679{col 38}{space 1}   -4.04{col 47}{space 3}0.000{col 55}{space 4}-.1595182{col 68}{space 3}-.0551533
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2}-.1295592{col 27}{space 2} .1305496{col 38}{space 1}   -0.99{col 47}{space 3}0.321{col 55}{space 4}-.3859733{col 68}{space 3} .1268549
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2} .0710678{col 27}{space 2} .1267438{col 38}{space 1}    0.56{col 47}{space 3}0.575{col 55}{space 4}-.1778713{col 68}{space 3}  .320007
{txt}{space 8}f_age {c |}{col 15}{res}{space 2}-.0100924{col 27}{space 2} .0081044{col 38}{space 1}   -1.25{col 47}{space 3}0.214{col 55}{space 4}-.0260104{col 68}{space 3} .0058255
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0527072{col 27}{space 2} .1234486{col 38}{space 1}   -0.43{col 47}{space 3}0.670{col 55}{space 4}-.2951742{col 68}{space 3} .1897597
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1477398{col 27}{space 2} .1340448{col 38}{space 1}    1.10{col 47}{space 3}0.271{col 55}{space 4}-.1155393{col 68}{space 3} .4110189
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1682977{col 27}{space 2} .0795296{col 38}{space 1}    2.12{col 47}{space 3}0.035{col 55}{space 4} .0120926{col 68}{space 3} .3245029
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.281674{col 27}{space 2} .4343194{col 38}{space 1}    2.95{col 47}{space 3}0.003{col 55}{space 4} .4286219{col 68}{space 3} 2.134726
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. estimates store est_t3_col4
. 
. suest est_t3_col2 est_t3_col3 est_t3_col4
{res}
{txt}Simultaneous results for {stata estimates replay est_t3_col2:est_t3_col2}, {stata estimates replay est_t3_col3:est_t3_col3}, {stata estimates replay est_t3_col4:est_t3_col4}

{col 49}Number of obs{col 67}= {res}       921

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}est_t3_col2_mean  {txt}{c |}
{space 4}ln_wage_m_rel {c |}{col 19}{res}{space 2} .7113864{col 31}{space 2} .1691903{col 42}{space 1}    4.20{col 51}{space 3}0.000{col 59}{space 4} .3797796{col 72}{space 3} 1.042993
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.1620978{col 31}{space 2} .0404373{col 42}{space 1}   -4.01{col 51}{space 3}0.000{col 59}{space 4}-.2413535{col 72}{space 3}-.0828421
{txt}{space 8}med_scoll {c |}{col 19}{res}{space 2} .1982918{col 31}{space 2} .1687373{col 42}{space 1}    1.18{col 51}{space 3}0.240{col 59}{space 4}-.1324273{col 72}{space 3} .5290109
{txt}{space 5}med_collplus {c |}{col 19}{res}{space 2} .0087094{col 31}{space 2} .2490637{col 42}{space 1}    0.03{col 51}{space 3}0.972{col 59}{space 4}-.4794464{col 72}{space 3} .4968652
{txt}{space 12}m_age {c |}{col 19}{res}{space 2}-.0137502{col 31}{space 2} .0132627{col 42}{space 1}   -1.04{col 51}{space 3}0.300{col 59}{space 4}-.0397445{col 72}{space 3} .0122441
{txt}{space 10}m_white {c |}{col 19}{res}{space 2}-.4133996{col 31}{space 2} .1599532{col 42}{space 1}   -2.58{col 51}{space 3}0.010{col 59}{space 4} -.726902{col 72}{space 3}-.0998972
{txt}{space 10}num_0_5 {c |}{col 19}{res}{space 2}-.1390867{col 31}{space 2} .1935027{col 42}{space 1}   -0.72{col 51}{space 3}0.472{col 59}{space 4} -.518345{col 72}{space 3} .2401716
{txt}{space 8}num_child {c |}{col 19}{res}{space 2} .0805871{col 31}{space 2} .1034539{col 42}{space 1}    0.78{col 51}{space 3}0.436{col 59}{space 4}-.1221787{col 72}{space 3}  .283353
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.470977{col 31}{space 2} .6406051{col 42}{space 1}    3.86{col 51}{space 3}0.000{col 59}{space 4} 1.215414{col 72}{space 3}  3.72654
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}est_t3_col2_lnvar {txt}{c |}
{space 12}_cons {c |}{col 19}{res}{space 2} .2719627{col 31}{space 2} .0882216{col 42}{space 1}    3.08{col 51}{space 3}0.002{col 59}{space 4} .0990515{col 72}{space 3} .4448738
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}est_t3_col3_mean  {txt}{c |}
{space 4}ln_wage_m_rel {c |}{col 19}{res}{space 2}  .627938{col 31}{space 2} .0785207{col 42}{space 1}    8.00{col 51}{space 3}0.000{col 59}{space 4} .4740404{col 72}{space 3} .7818357
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.1316055{col 31}{space 2} .0255821{col 42}{space 1}   -5.14{col 51}{space 3}0.000{col 59}{space 4}-.1817455{col 72}{space 3}-.0814654
{txt}{space 8}med_scoll {c |}{col 19}{res}{space 2}-.1243583{col 31}{space 2}  .126404{col 42}{space 1}   -0.98{col 51}{space 3}0.325{col 59}{space 4}-.3721056{col 72}{space 3} .1233889
{txt}{space 5}med_collplus {c |}{col 19}{res}{space 2}-.2687168{col 31}{space 2} .1318531{col 42}{space 1}   -2.04{col 51}{space 3}0.042{col 59}{space 4}-.5271442{col 72}{space 3}-.0102894
{txt}{space 12}m_age {c |}{col 19}{res}{space 2}-.0053975{col 31}{space 2} .0097853{col 42}{space 1}   -0.55{col 51}{space 3}0.581{col 59}{space 4}-.0245763{col 72}{space 3} .0137814
{txt}{space 10}m_white {c |}{col 19}{res}{space 2}-.1699007{col 31}{space 2} .1089735{col 42}{space 1}   -1.56{col 51}{space 3}0.119{col 59}{space 4}-.3834849{col 72}{space 3} .0436835
{txt}{space 10}num_0_5 {c |}{col 19}{res}{space 2} .2910007{col 31}{space 2} .1393158{col 42}{space 1}    2.09{col 51}{space 3}0.037{col 59}{space 4} .0179468{col 72}{space 3} .5640545
{txt}{space 8}num_child {c |}{col 19}{res}{space 2} .1068818{col 31}{space 2} .0727997{col 42}{space 1}    1.47{col 51}{space 3}0.142{col 59}{space 4} -.035803{col 72}{space 3} .2495667
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.981708{col 31}{space 2} .4166812{col 42}{space 1}    4.76{col 51}{space 3}0.000{col 59}{space 4} 1.165028{col 72}{space 3} 2.798388
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}est_t3_col3_lnvar {txt}{c |}
{space 12}_cons {c |}{col 19}{res}{space 2} .0775375{col 31}{space 2}  .059376{col 42}{space 1}    1.31{col 51}{space 3}0.192{col 59}{space 4}-.0388374{col 72}{space 3} .1939124
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}est_t3_col4_mean  {txt}{c |}
{space 4}ln_wage_f_rel {c |}{col 19}{res}{space 2} .6778651{col 31}{space 2} .0890412{col 42}{space 1}    7.61{col 51}{space 3}0.000{col 59}{space 4} .5033476{col 72}{space 3} .8523826
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.1073358{col 31}{space 2} .0269415{col 42}{space 1}   -3.98{col 51}{space 3}0.000{col 59}{space 4}-.1601402{col 72}{space 3}-.0545314
{txt}{space 8}fed_scoll {c |}{col 19}{res}{space 2}-.1295592{col 31}{space 2} .1305634{col 42}{space 1}   -0.99{col 51}{space 3}0.321{col 59}{space 4}-.3854587{col 72}{space 3} .1263403
{txt}{space 5}fed_collplus {c |}{col 19}{res}{space 2} .0710678{col 31}{space 2}   .12786{col 42}{space 1}    0.56{col 51}{space 3}0.578{col 59}{space 4}-.1795331{col 72}{space 3} .3216687
{txt}{space 12}f_age {c |}{col 19}{res}{space 2}-.0100924{col 31}{space 2} .0078644{col 42}{space 1}   -1.28{col 51}{space 3}0.199{col 59}{space 4}-.0255064{col 72}{space 3} .0053215
{txt}{space 10}m_white {c |}{col 19}{res}{space 2}-.0527072{col 31}{space 2}  .124049{col 42}{space 1}   -0.42{col 51}{space 3}0.671{col 59}{space 4}-.2958389{col 72}{space 3} .1904244
{txt}{space 10}num_0_5 {c |}{col 19}{res}{space 2} .1477398{col 31}{space 2} .1343747{col 42}{space 1}    1.10{col 51}{space 3}0.272{col 59}{space 4}-.1156297{col 72}{space 3} .4111094
{txt}{space 8}num_child {c |}{col 19}{res}{space 2} .1682977{col 31}{space 2} .0685017{col 42}{space 1}    2.46{col 51}{space 3}0.014{col 59}{space 4} .0340369{col 72}{space 3} .3025586
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.281674{col 31}{space 2} .4244042{col 42}{space 1}    3.02{col 51}{space 3}0.003{col 59}{space 4} .4498571{col 72}{space 3} 2.113491
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}est_t3_col4_lnvar {txt}{c |}
{space 12}_cons {c |}{col 19}{res}{space 2} .2958971{col 31}{space 2} .0558736{col 42}{space 1}    5.30{col 51}{space 3}0.000{col 59}{space 4} .1863868{col 72}{space 3} .4054075
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. 
. *Test for equality of log wage coefficients across columns 2-4
. test [est_t3_col2_mean]ln_wage_m_rel = [est_t3_col3_mean]ln_wage_m_rel = [est_t3_col4_mean]ln_wage_f_rel

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[est_t3_col2_mean]ln_wage_m_rel - [est_t3_col3_mean]ln_wage_m_rel = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [est_t3_col2_mean]ln_wage_m_rel - [est_t3_col4_mean]ln_wage_f_rel = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    0.31
{txt}{col 10}Prob > chi2 =  {res}  0.8579
{com}. 
. *Joint test for equality of remaining coefficients across columns 2-4
. test ([est_t3_col2_mean]age = [est_t3_col3_mean]age = [est_t3_col4_mean]age) ([est_t3_col2_mean]med_scoll = [est_t3_col3_mean]med_scoll = [est_t3_col4_mean]fed_scoll) ([est_t3_col2_mean]med_collplus = [est_t3_col3_mean]med_collplus = [est_t3_col4_mean]fed_collplus) ([est_t3_col2_mean]m_age = [est_t3_col3_mean]m_age = [est_t3_col4_mean]f_age) ([est_t3_col2_mean]m_white = [est_t3_col3_mean]m_white = [est_t3_col4_mean]m_white) ([est_t3_col2_mean]num_0_5 = [est_t3_col3_mean]num_0_5 = [est_t3_col4_mean]num_0_5) ([est_t3_col2_mean]num_child = [est_t3_col3_mean]num_child = [est_t3_col4_mean]num_child) ([est_t3_col2_lnvar]_cons = [est_t3_col3_lnvar]_cons = [est_t3_col4_lnvar]_cons)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[est_t3_col2_mean]age - [est_t3_col3_mean]age = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [est_t3_col2_mean]age - [est_t3_col4_mean]age = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [est_t3_col2_mean]med_scoll - [est_t3_col3_mean]med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [est_t3_col2_mean]med_scoll - [est_t3_col4_mean]fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [est_t3_col2_mean]med_collplus - [est_t3_col3_mean]med_collplus = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} [est_t3_col2_mean]med_collplus - [est_t3_col4_mean]fed_collplus = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} [est_t3_col2_mean]m_age - [est_t3_col3_mean]m_age = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} [est_t3_col2_mean]m_age - [est_t3_col4_mean]f_age = 0{p_end}
{p 0 7}{space 1}{text:( 9)}{space 1} [est_t3_col2_mean]m_white - [est_t3_col3_mean]m_white = 0{p_end}
{p 0 7}{space 1}{text:(10)}{space 1} [est_t3_col2_mean]m_white - [est_t3_col4_mean]m_white = 0{p_end}
{p 0 7}{space 1}{text:(11)}{space 1} [est_t3_col2_mean]num_0_5 - [est_t3_col3_mean]num_0_5 = 0{p_end}
{p 0 7}{space 1}{text:(12)}{space 1} [est_t3_col2_mean]num_0_5 - [est_t3_col4_mean]num_0_5 = 0{p_end}
{p 0 7}{space 1}{text:(13)}{space 1} [est_t3_col2_mean]num_child - [est_t3_col3_mean]num_child = 0{p_end}
{p 0 7}{space 1}{text:(14)}{space 1} [est_t3_col2_mean]num_child - [est_t3_col4_mean]num_child = 0{p_end}
{p 0 7}{space 1}{text:(15)}{space 1} [est_t3_col2_lnvar]_cons - [est_t3_col3_lnvar]_cons = 0{p_end}
{p 0 7}{space 1}{text:(16)}{space 1} [est_t3_col2_lnvar]_cons - [est_t3_col4_lnvar]_cons = 0{p_end}

{txt}{col 12}chi2( 16) ={res}   29.89
{txt}{col 10}Prob > chi2 =  {res}  0.0186
{com}. 
. *Joint test for equality of all coefficients across columns 2-4
. test ([est_t3_col2_mean]ln_wage_m_rel = [est_t3_col3_mean]ln_wage_m_rel = [est_t3_col4_mean]ln_wage_f_rel) ([est_t3_col2_mean]age = [est_t3_col3_mean]age = [est_t3_col4_mean]age) ([est_t3_col2_mean]med_scoll = [est_t3_col3_mean]med_scoll = [est_t3_col4_mean]fed_scoll) ([est_t3_col2_mean]med_collplus = [est_t3_col3_mean]med_collplus = [est_t3_col4_mean]fed_collplus) ([est_t3_col2_mean]m_age = [est_t3_col3_mean]m_age = [est_t3_col4_mean]f_age) ([est_t3_col2_mean]m_white = [est_t3_col3_mean]m_white = [est_t3_col4_mean]m_white) ([est_t3_col2_mean]num_0_5 = [est_t3_col3_mean]num_0_5 = [est_t3_col4_mean]num_0_5) ([est_t3_col2_mean]num_child = [est_t3_col3_mean]num_child = [est_t3_col4_mean]num_child) ([est_t3_col2_lnvar]_cons = [est_t3_col3_lnvar]_cons = [est_t3_col4_lnvar]_cons)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[est_t3_col2_mean]ln_wage_m_rel - [est_t3_col3_mean]ln_wage_m_rel = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [est_t3_col2_mean]ln_wage_m_rel - [est_t3_col4_mean]ln_wage_f_rel = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [est_t3_col2_mean]age - [est_t3_col3_mean]age = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [est_t3_col2_mean]age - [est_t3_col4_mean]age = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [est_t3_col2_mean]med_scoll - [est_t3_col3_mean]med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} [est_t3_col2_mean]med_scoll - [est_t3_col4_mean]fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} [est_t3_col2_mean]med_collplus - [est_t3_col3_mean]med_collplus = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} [est_t3_col2_mean]med_collplus - [est_t3_col4_mean]fed_collplus = 0{p_end}
{p 0 7}{space 1}{text:( 9)}{space 1} [est_t3_col2_mean]m_age - [est_t3_col3_mean]m_age = 0{p_end}
{p 0 7}{space 1}{text:(10)}{space 1} [est_t3_col2_mean]m_age - [est_t3_col4_mean]f_age = 0{p_end}
{p 0 7}{space 1}{text:(11)}{space 1} [est_t3_col2_mean]m_white - [est_t3_col3_mean]m_white = 0{p_end}
{p 0 7}{space 1}{text:(12)}{space 1} [est_t3_col2_mean]m_white - [est_t3_col4_mean]m_white = 0{p_end}
{p 0 7}{space 1}{text:(13)}{space 1} [est_t3_col2_mean]num_0_5 - [est_t3_col3_mean]num_0_5 = 0{p_end}
{p 0 7}{space 1}{text:(14)}{space 1} [est_t3_col2_mean]num_0_5 - [est_t3_col4_mean]num_0_5 = 0{p_end}
{p 0 7}{space 1}{text:(15)}{space 1} [est_t3_col2_mean]num_child - [est_t3_col3_mean]num_child = 0{p_end}
{p 0 7}{space 1}{text:(16)}{space 1} [est_t3_col2_mean]num_child - [est_t3_col4_mean]num_child = 0{p_end}
{p 0 7}{space 1}{text:(17)}{space 1} [est_t3_col2_lnvar]_cons - [est_t3_col3_lnvar]_cons = 0{p_end}
{p 0 7}{space 1}{text:(18)}{space 1} [est_t3_col2_lnvar]_cons - [est_t3_col4_lnvar]_cons = 0{p_end}

{txt}{col 12}chi2( 18) ={res}   33.07
{txt}{col 10}Prob > chi2 =  {res}  0.0163
{com}. 
. *Joint test for equality of all coefficients but intercept and coefficient on log relative wages across columns 2-4
. test ([est_t3_col2_mean]age = [est_t3_col3_mean]age = [est_t3_col4_mean]age) ([est_t3_col2_mean]med_scoll = [est_t3_col3_mean]med_scoll = [est_t3_col4_mean]fed_scoll) ([est_t3_col2_mean]med_collplus = [est_t3_col3_mean]med_collplus = [est_t3_col4_mean]fed_collplus) ([est_t3_col2_mean]m_age = [est_t3_col3_mean]m_age = [est_t3_col4_mean]f_age) ([est_t3_col2_mean]m_white = [est_t3_col3_mean]m_white = [est_t3_col4_mean]m_white) ([est_t3_col2_mean]num_0_5 = [est_t3_col3_mean]num_0_5 = [est_t3_col4_mean]num_0_5) ([est_t3_col2_mean]num_child = [est_t3_col3_mean]num_child = [est_t3_col4_mean]num_child)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[est_t3_col2_mean]age - [est_t3_col3_mean]age = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [est_t3_col2_mean]age - [est_t3_col4_mean]age = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [est_t3_col2_mean]med_scoll - [est_t3_col3_mean]med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [est_t3_col2_mean]med_scoll - [est_t3_col4_mean]fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [est_t3_col2_mean]med_collplus - [est_t3_col3_mean]med_collplus = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} [est_t3_col2_mean]med_collplus - [est_t3_col4_mean]fed_collplus = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} [est_t3_col2_mean]m_age - [est_t3_col3_mean]m_age = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} [est_t3_col2_mean]m_age - [est_t3_col4_mean]f_age = 0{p_end}
{p 0 7}{space 1}{text:( 9)}{space 1} [est_t3_col2_mean]m_white - [est_t3_col3_mean]m_white = 0{p_end}
{p 0 7}{space 1}{text:(10)}{space 1} [est_t3_col2_mean]m_white - [est_t3_col4_mean]m_white = 0{p_end}
{p 0 7}{space 1}{text:(11)}{space 1} [est_t3_col2_mean]num_0_5 - [est_t3_col3_mean]num_0_5 = 0{p_end}
{p 0 7}{space 1}{text:(12)}{space 1} [est_t3_col2_mean]num_0_5 - [est_t3_col4_mean]num_0_5 = 0{p_end}
{p 0 7}{space 1}{text:(13)}{space 1} [est_t3_col2_mean]num_child - [est_t3_col3_mean]num_child = 0{p_end}
{p 0 7}{space 1}{text:(14)}{space 1} [est_t3_col2_mean]num_child - [est_t3_col4_mean]num_child = 0{p_end}

{txt}{col 12}chi2( 14) ={res}   20.35
{txt}{col 10}Prob > chi2 =  {res}  0.1195
{com}. 
. *Test for equality of intercepts across columns 2-4
. test ([est_t3_col2_lnvar]_cons = [est_t3_col3_lnvar]_cons = [est_t3_col4_lnvar]_cons)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[est_t3_col2_lnvar]_cons - [est_t3_col3_lnvar]_cons = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [est_t3_col2_lnvar]_cons - [est_t3_col4_lnvar]_cons = 0{p_end}

{txt}{col 12}chi2(  2) ={res}    9.28
{txt}{col 10}Prob > chi2 =  {res}  0.0097
{com}. 
. *Joint test for equality of all coefficients but intercept across columns 2-4
. test ([est_t3_col2_mean]ln_wage_m_rel = [est_t3_col3_mean]ln_wage_m_rel = [est_t3_col4_mean]ln_wage_f_rel) ([est_t3_col2_mean]age = [est_t3_col3_mean]age = [est_t3_col4_mean]age) ([est_t3_col2_mean]med_scoll = [est_t3_col3_mean]med_scoll = [est_t3_col4_mean]fed_scoll) ([est_t3_col2_mean]med_collplus = [est_t3_col3_mean]med_collplus = [est_t3_col4_mean]fed_collplus) ([est_t3_col2_mean]m_age = [est_t3_col3_mean]m_age = [est_t3_col4_mean]f_age) ([est_t3_col2_mean]m_white = [est_t3_col3_mean]m_white = [est_t3_col4_mean]m_white) ([est_t3_col2_mean]num_0_5 = [est_t3_col3_mean]num_0_5 = [est_t3_col4_mean]num_0_5) ([est_t3_col2_mean]num_child = [est_t3_col3_mean]num_child = [est_t3_col4_mean]num_child)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[est_t3_col2_mean]ln_wage_m_rel - [est_t3_col3_mean]ln_wage_m_rel = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [est_t3_col2_mean]ln_wage_m_rel - [est_t3_col4_mean]ln_wage_f_rel = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [est_t3_col2_mean]age - [est_t3_col3_mean]age = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [est_t3_col2_mean]age - [est_t3_col4_mean]age = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [est_t3_col2_mean]med_scoll - [est_t3_col3_mean]med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} [est_t3_col2_mean]med_scoll - [est_t3_col4_mean]fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} [est_t3_col2_mean]med_collplus - [est_t3_col3_mean]med_collplus = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} [est_t3_col2_mean]med_collplus - [est_t3_col4_mean]fed_collplus = 0{p_end}
{p 0 7}{space 1}{text:( 9)}{space 1} [est_t3_col2_mean]m_age - [est_t3_col3_mean]m_age = 0{p_end}
{p 0 7}{space 1}{text:(10)}{space 1} [est_t3_col2_mean]m_age - [est_t3_col4_mean]f_age = 0{p_end}
{p 0 7}{space 1}{text:(11)}{space 1} [est_t3_col2_mean]m_white - [est_t3_col3_mean]m_white = 0{p_end}
{p 0 7}{space 1}{text:(12)}{space 1} [est_t3_col2_mean]m_white - [est_t3_col4_mean]m_white = 0{p_end}
{p 0 7}{space 1}{text:(13)}{space 1} [est_t3_col2_mean]num_0_5 - [est_t3_col3_mean]num_0_5 = 0{p_end}
{p 0 7}{space 1}{text:(14)}{space 1} [est_t3_col2_mean]num_0_5 - [est_t3_col4_mean]num_0_5 = 0{p_end}
{p 0 7}{space 1}{text:(15)}{space 1} [est_t3_col2_mean]num_child - [est_t3_col3_mean]num_child = 0{p_end}
{p 0 7}{space 1}{text:(16)}{space 1} [est_t3_col2_mean]num_child - [est_t3_col4_mean]num_child = 0{p_end}

{txt}{col 12}chi2( 16) ={res}   24.25
{txt}{col 10}Prob > chi2 =  {res}  0.0841
{com}. 
. *Joint test for equality of all coefficients but intercept across columns 2 and 3
. test ([est_t3_col2_mean]ln_wage_m_rel = [est_t3_col3_mean]ln_wage_m_rel) ([est_t3_col2_mean]age = [est_t3_col3_mean]age) ([est_t3_col2_mean]med_scoll = [est_t3_col3_mean]med_scoll) ([est_t3_col2_mean]med_collplus = [est_t3_col3_mean]med_collplus) ([est_t3_col2_mean]m_age = [est_t3_col3_mean]m_age) ([est_t3_col2_mean]m_white = [est_t3_col3_mean]m_white) ([est_t3_col2_mean]num_0_5 = [est_t3_col3_mean]num_0_5) ([est_t3_col2_mean]num_child = [est_t3_col3_mean]num_child)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[est_t3_col2_mean]ln_wage_m_rel - [est_t3_col3_mean]ln_wage_m_rel = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [est_t3_col2_mean]age - [est_t3_col3_mean]age = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [est_t3_col2_mean]med_scoll - [est_t3_col3_mean]med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [est_t3_col2_mean]med_collplus - [est_t3_col3_mean]med_collplus = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [est_t3_col2_mean]m_age - [est_t3_col3_mean]m_age = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} [est_t3_col2_mean]m_white - [est_t3_col3_mean]m_white = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} [est_t3_col2_mean]num_0_5 - [est_t3_col3_mean]num_0_5 = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} [est_t3_col2_mean]num_child - [est_t3_col3_mean]num_child = 0{p_end}

{txt}{col 12}chi2(  8) ={res}    7.48
{txt}{col 10}Prob > chi2 =  {res}  0.4863
{com}. 
. 
. 
. ******* Super-summary Tables for all Parent types (spec 5 and 7)
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Table 3: OLS estimates for parental time vs. goods relative demand, by parent type
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab estm_ols5 estms_ols5 estmm_ols5 estf_ols5 using "$tables/Parents_5a.tex", rename (ln_wage_f_rel ln_wage_m_rel) coeflab(ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}j,i{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children"  fed_hs "Father HS grad"  fed_scoll "Father some coll."  fed_collplus "Father coll+" f_age "Father's age") stats(r2 N, fmt(3 0))  ti(Table: OLS estimates for parental time vs.\ goods relative demand (1-2 kids ages 0-12, P(work) $\geq$ `p_work_min_m' or `p_work_min_f', 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(20) modelwidth(12) wrap style(tex) mtitles("All Mother" "Single Mother" "Married Mother" "Married Father")   replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/Parents_5a.tex"'})
{com}. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-7: 2SLS estimates for parental time vs. goods relative demand
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab estm_ivpw5 estms_ivpw5 estmm_ivpw5 estf_ivpw5 using "$tables/Parents_5c.tex", rename (ln_wage_f_rel ln_wage_m_rel)  coeflab(ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}j,i{c )-})$"  curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children"  fed_hs "Father HS grad"  fed_scoll "Father some coll."  fed_collplus "Father coll+" f_age "Father's age") stats(r2 N, fmt(3 0))  ti(Appendix Table: IV (predicted wage) estimates for parental time vs.\ goods relative demand (1-2 kids ages 0-12, P(work) $\geq$ `p_work_min_m' or `p_work_min_f', 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(20) modelwidth(12) wrap style(tex) mtitles("All Mother" "Single Mother" "Married Mother" "Married Father")    replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/Parents_5c.tex"'})
{com}. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-8: OLS estimates for parental time vs. goods relative demand including parental 
. *** log wage fixed effects, by parent type
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab estm_ols7 estms_ols7 estmm_ols7 estf_ols7 using "$tables/Parents_7a.tex", rename (ln_wage_f_rel ln_wage_m_rel) coeflab(ln_wage_m_rel "$\ln(\tilde{c -(}W{c )-}_{c -(}j,i{c )-})$" curr_married "Married"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"  m_age "Mother's age" m_white "Mother white"  num_0_5 "Num children age 0-5" num_child "Num of children"  fed_hs "Father HS grad"  fed_scoll "Father some coll."  fed_collplus "Father coll+" f_age "Father's age" ln_wageFE_m "Mother's log wage FE" ln_wageFE_f "Father's log wage FE")  stats(r2 N, fmt(3 0))  ti(Appendix Table: OLS estimates for parental time vs.\ goods relative demand (1-2 kids ages 0-12, P(work) $\geq$ `p_work_min_m' or `p_work_min_f', 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(20) modelwidth(12) wrap style(tex) mtitles("All Mother" "Single Mother" "Married Mother" "Married Father")  replace
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/Parents_7a.tex"'})
{com}. 
. 
. {c )-}
{txt}
{com}. 
. 
. 
. 
. 
. 
. if $est_chcare_goods==1 {c -(}
. 
. clear all
{res}{com}. 
. 
. ****************************************************************************************************
. ******************** F. ESTIMATION: CHILD CARE VS. HOUSEHOLD GOODS  ********************************
. ****************************************************************************************************
. 
. 
. *********** SINGLE MOTHERS (2002, 2007) ************
. 
. clear
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. keep if curr_married==0
{txt}(1,004 observations deleted)
{com}. 
. * keep if prob_work_m >= `p_work_min_m' & prob_work_m !=. //restrict to high work probability
. 
. 
. * first stage specification for time vs. goods (use only those with high probability of work, but generate predicted probability for all)
. 
. local Zmspec = "5"
. 
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel  `Zm`Zmspec''       if prob_work_m >= `p_work_min_m' & prob_work_m !=.

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       236
{txt}{hline 13}{c +}{hline 34}   F(8, 227)       = {res}     6.98
{txt}       Model {c |} {res} 73.2717766         8  9.15897207   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 297.946123       227  1.31253799   {txt}R-squared       ={res}    0.1974
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1691
{txt}       Total {c |} {res} 371.217899       235  1.57965064   {txt}Root MSE        =   {res} 1.1457

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .7113864{col 27}{space 2} .1551512{col 38}{space 1}    4.59{col 47}{space 3}0.000{col 55}{space 4} .4056658{col 68}{space 3} 1.017107
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1620978{col 27}{space 2} .0428433{col 38}{space 1}   -3.78{col 47}{space 3}0.000{col 55}{space 4}-.2465193{col 68}{space 3}-.0776763
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1982918{col 27}{space 2} .1732925{col 38}{space 1}    1.14{col 47}{space 3}0.254{col 55}{space 4}-.1431757{col 68}{space 3} .5397594
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .0087094{col 27}{space 2} .2224559{col 38}{space 1}    0.04{col 47}{space 3}0.969{col 55}{space 4}-.4296331{col 68}{space 3} .4470519
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0137502{col 27}{space 2}   .01374{col 38}{space 1}   -1.00{col 47}{space 3}0.318{col 55}{space 4}-.0408246{col 68}{space 3} .0133241
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.4133996{col 27}{space 2} .1667903{col 38}{space 1}   -2.48{col 47}{space 3}0.014{col 55}{space 4}-.7420548{col 68}{space 3}-.0847444
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.1390867{col 27}{space 2} .2387813{col 38}{space 1}   -0.58{col 47}{space 3}0.561{col 55}{space 4} -.609598{col 68}{space 3} .3314246
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0805871{col 27}{space 2} .1092916{col 38}{space 1}    0.74{col 47}{space 3}0.462{col 55}{space 4}-.1347686{col 68}{space 3} .2959428
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.470977{col 27}{space 2} .6907348{col 38}{space 1}    3.58{col 47}{space 3}0.000{col 55}{space 4} 1.109905{col 68}{space 3} 3.832049
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. predict lnPHIms_ols`Zmspec', xb      // predicted time relative to good expenditures, Phi_m, for single mothers
{txt}(137 missing values generated)
{com}. 
. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.464841{col 26}{space 2} 1.862608{col 37}{space 1}   -1.32{col 46}{space 3}0.186{col 54}{space 4}-6.115486{col 67}{space 3} 1.185805
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. local rhom_ols`Zmspec' = _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)
. 
. summ lnPHIms_ols`Zmspec' ln_tau_m_g_exp_ratio

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
lnPHIms_ols5 {c |}{res}        371    2.175519    .5826008  -.7268998   3.553663
{txt}ln_tau_m_g.. {c |}{res}        267     2.19366    1.260382  -2.867899   4.795085
{com}. 
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel  `Zm`Zmspec''        if (ln_hhinvest !=. & ln_invratio_imp !=.)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}        94
{txt}{hline 13}{c +}{hline 34}   F(8, 85)        = {res}     2.78
{txt}       Model {c |} {res} 23.1562844         8  2.89453555   {txt}Prob > F        ={res}    0.0089
{txt}    Residual {c |} {res} 88.6328202        85  1.04273906   {txt}R-squared       ={res}    0.2071
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1325
{txt}       Total {c |} {res} 111.789105        93  1.20203338   {txt}Root MSE        =   {res} 1.0211

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .4872431{col 27}{space 2} .2832361{col 38}{space 1}    1.72{col 47}{space 3}0.089{col 55}{space 4}-.0759061{col 68}{space 3} 1.050392
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1812755{col 27}{space 2} .0580369{col 38}{space 1}   -3.12{col 47}{space 3}0.002{col 55}{space 4}-.2966685{col 68}{space 3}-.0658826
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .4358695{col 27}{space 2} .2662272{col 38}{space 1}    1.64{col 47}{space 3}0.105{col 55}{space 4}-.0934616{col 68}{space 3} .9652006
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .2966142{col 27}{space 2} .3177222{col 38}{space 1}    0.93{col 47}{space 3}0.353{col 55}{space 4}-.3351026{col 68}{space 3} .9283311
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0128186{col 27}{space 2} .0179828{col 38}{space 1}   -0.71{col 47}{space 3}0.478{col 55}{space 4}-.0485733{col 68}{space 3}  .022936
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2227396{col 27}{space 2} .2433526{col 38}{space 1}   -0.92{col 47}{space 3}0.363{col 55}{space 4}-.7065898{col 68}{space 3} .2611106
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} -.356039{col 27}{space 2} .2681617{col 38}{space 1}   -1.33{col 47}{space 3}0.188{col 55}{space 4}-.8892163{col 68}{space 3} .1771383
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0983897{col 27}{space 2} .1669084{col 38}{space 1}    0.59{col 47}{space 3}0.557{col 55}{space 4}-.2334688{col 68}{space 3} .4302483
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  3.10419{col 27}{space 2} .9522105{col 38}{space 1}    3.26{col 47}{space 3}0.002{col 55}{space 4} 1.210941{col 68}{space 3}  4.99744
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. nlcom rho: _b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

         {txt}rho:  {res}_b[ln_wage_m_rel]/(_b[ln_wage_m_rel] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g..{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} -.950242{col 26}{space 2} 1.077273{col 37}{space 1}   -0.88{col 46}{space 3}0.378{col 54}{space 4}-3.061657{col 67}{space 3} 1.161173
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. 
. 
. * Generate nonlinear terms for below
. 
. gen logtermPHI = ln(1.0 + exp(lnPHIms_ols`Zmspec'))
{txt}(137 missing values generated)
{com}.         
. gen temp    = 1.0 + exp(ln_tau_m_g_exp_ratio)
{txt}(241 missing values generated)
{com}. gen logterm = ln(temp)
{txt}(241 missing values generated)
{com}. gen term2   = exp(ln_tau_m_g_exp_ratio)/(2.0*temp*temp) 
{txt}(241 missing values generated)
{com}. 
. summ logtermPHI temp logterm term2

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}logtermPHI {c |}{res}        371    2.301133    .4978787   .3943403   3.581881
{txt}{space 8}temp {c |}{res}        267    18.66143    21.98718   1.056818   121.9146
{txt}{space 5}logterm {c |}{res}        267     2.38534    1.043832   .0552627   4.803321
{txt}{space 7}term2 {c |}{res}        267    .0516712     .035346   .0040676   .1249557
{com}. 
. 
. * OLS estimation of child care vs. goods equation only
. 
. foreach pvar in 4ca{c -(}
{txt}  2{com}. 
.     disp  _n(3) "SINGLE MOTHERS, CHILD CARE VS. GOODS" 
{txt}  3{com}.     disp  _n(1) "Price measure: `pvar'" 
{txt}  4{com}.         disp        "first stage specification Zm`Zmspec'" _n(2)
{txt}  5{com}.         
. * OLS estimates
. 
.         forvalues zval=1/7 {c -(}
{txt}  6{com}.                 regress ln_invratio_imp  ln_pratio_`pvar' `Zm`zval''
{txt}  7{com}.                 nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt}  8{com}.                 local gam_S_ols`zval' = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt}  9{com}.                 capture noisily test `medvars'
{txt} 10{com}.             capture noisily test med_scoll med_collplus
{txt} 11{com}.                 estimates store estS_ols`zval'
{txt} 12{com}.         {c )-}
{txt} 13{com}.         
.         
.         * get a de-meaned and re-scaled value of ln_hhinvest for relevant sample for regressor in Y vs. g relative demand equation -- (ln(g)-E[ln(g)])/Var(ln(g)) 
.                 
.         gen nonmiss_Yvg = (ln_invratio_imp !=. & ln_tau_m_g_exp_ratio !=. & ln_pratio_`pvar' !=. & ln_hhinv !=.)  // note that logterm and term2 variables are functions of ln_tau_m_g_exp_ratio
{txt} 14{com}.         foreach zvar in `Zm5'{c -(}
{txt} 15{com}.                 replace nonmiss_Yvg=0 if `zvar'==.     // setting indicator to zero if variable is missing
{txt} 16{com}.         {c )-}
{txt} 17{com}. 
.         summ ln_hhinvest if (nonmiss_Yvg==1)              
{txt} 18{com}.         gen d_ln_hhinv = (ln_hhinvest - r(mean))/r(Var)   
{txt} 19{com}.         summ ln_hhinvest d_ln_hhinv     
{txt} 20{com}.         
.         
.         disp _n(3) "OLS with nonlinear terms"   
{txt} 21{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zm5' logterm                   // no input measurement error case
{txt} 22{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 23{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logterm])
{txt} 24{com}.         capture noisily test med_scoll med_collplus
{txt} 25{com}.         local gam_S_log = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 26{com}.         estimates store estS_log
{txt} 27{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zm5' logterm term2 d_ln_hhinv  // includes terms for input measurement error case
{txt} 28{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 29{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logterm])
{txt} 30{com}.         capture noisily test med_scoll med_collplus
{txt} 31{com}.         local gam_S_ME = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 32{com}.         estimates store estS_ME         
{txt} 33{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zm5' logtermPHI                // includes predicted relative time/goods expenditure
{txt} 34{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 35{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logtermPHI])
{txt} 36{com}.         capture noisily test med_scoll med_collplus
{txt} 37{com}.         local gam_S_PHI = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 38{com}.         estimates store estS_PHI
{txt} 39{com}.         
. 
.         drop d_ln_hhinv nonmiss_Yvg
{txt} 40{com}. 
. {c )-} // end loop over pvar



SINGLE MOTHERS, CHILD CARE VS. GOODS

Price measure: 4ca
first stage specification Zm5



{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       121
{txt}{hline 13}{c +}{hline 34}   F(1, 119)       = {res}     5.10
{txt}       Model {c |} {res} 7.17272936         1  7.17272936   {txt}Prob > F        ={res}    0.0257
{txt}    Residual {c |} {res}  167.30251       119  1.40590345   {txt}R-squared       ={res}    0.0411
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0331
{txt}       Total {c |} {res} 174.475239       120  1.45396033   {txt}Root MSE        =   {res} 1.1857

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .6880401{col 27}{space 2} .3046136{col 38}{space 1}    2.26{col 47}{space 3}0.026{col 55}{space 4} .0848748{col 68}{space 3} 1.291205
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.1357724{col 27}{space 2} .3354743{col 38}{space 1}   -0.40{col 47}{space 3}0.686{col 55}{space 4}-.8000451{col 68}{space 3} .5285003
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2} -2.20554{col 26}{space 2} 3.130052{col 37}{space 1}   -0.70{col 46}{space 3}0.481{col 54}{space 4} -8.34033{col 67}{space 3}  3.92925
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       121
{txt}{hline 13}{c +}{hline 34}   F(4, 116)       = {res}     1.28
{txt}       Model {c |} {res} 7.37817881         4   1.8445447   {txt}Prob > F        ={res}    0.2817
{txt}    Residual {c |} {res} 167.097061       116   1.4404919   {txt}R-squared       ={res}    0.0423
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0093
{txt}       Total {c |} {res} 174.475239       120  1.45396033   {txt}Root MSE        =   {res} 1.2002

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .7054343{col 27}{space 2} .3186776{col 38}{space 1}    2.21{col 47}{space 3}0.029{col 55}{space 4} .0742531{col 68}{space 3} 1.336615
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2} .1117084{col 27}{space 2} .4937504{col 38}{space 1}    0.23{col 47}{space 3}0.821{col 55}{space 4}-.8662264{col 68}{space 3} 1.089643
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}  .166077{col 27}{space 2} .4923647{col 38}{space 1}    0.34{col 47}{space 3}0.736{col 55}{space 4}-.8091134{col 68}{space 3} 1.141267
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .1004209{col 27}{space 2} .5065249{col 38}{space 1}    0.20{col 47}{space 3}0.843{col 55}{space 4}-.9028155{col 68}{space 3} 1.103657
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.2764869{col 27}{space 2} .5902115{col 38}{space 1}   -0.47{col 47}{space 3}0.640{col 55}{space 4}-1.445475{col 68}{space 3} .8925014
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-2.394828{col 26}{space 2} 3.672714{col 37}{space 1}   -0.65{col 46}{space 3}0.514{col 54}{space 4}-9.593215{col 67}{space 3} 4.803559
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}       F(  3,   116) ={res}    0.05
{txt}{col 13}Prob > F ={res}    0.9862

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   116) ={res}    0.07
{txt}{col 13}Prob > F ={res}    0.9327

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       121
{txt}{hline 13}{c +}{hline 34}   F(6, 114)       = {res}     1.85
{txt}       Model {c |} {res} 15.5099403         6  2.58499005   {txt}Prob > F        ={res}    0.0949
{txt}    Residual {c |} {res} 158.965299       114  1.39443245   {txt}R-squared       ={res}    0.0889
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0409
{txt}       Total {c |} {res} 174.475239       120  1.45396033   {txt}Root MSE        =   {res} 1.1809

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .6549527{col 27}{space 2}  .317028{col 38}{space 1}    2.07{col 47}{space 3}0.041{col 55}{space 4} .0269227{col 68}{space 3} 1.282983
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1185305{col 27}{space 2} .0581612{col 38}{space 1}   -2.04{col 47}{space 3}0.044{col 55}{space 4}-.2337473{col 68}{space 3}-.0033137
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2} .1020776{col 27}{space 2} .4901653{col 38}{space 1}    0.21{col 47}{space 3}0.835{col 55}{space 4}-.8689361{col 68}{space 3} 1.073091
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .2995569{col 27}{space 2} .4951939{col 38}{space 1}    0.60{col 47}{space 3}0.546{col 55}{space 4}-.6814184{col 68}{space 3} 1.280532
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .1373407{col 27}{space 2} .5107861{col 38}{space 1}    0.27{col 47}{space 3}0.789{col 55}{space 4}-.8745226{col 68}{space 3} 1.149204
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0073423{col 27}{space 2} .0172278{col 38}{space 1}   -0.43{col 47}{space 3}0.671{col 55}{space 4}-.0414705{col 68}{space 3} .0267859
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .9994893{col 27}{space 2} .8074804{col 38}{space 1}    1.24{col 47}{space 3}0.218{col 55}{space 4}-.6001231{col 68}{space 3} 2.599102
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.898153{col 26}{space 2} 2.662811{col 37}{space 1}   -0.71{col 46}{space 3}0.476{col 54}{space 4}-7.117166{col 67}{space 3}  3.32086
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}       F(  3,   114) ={res}    0.25
{txt}{col 13}Prob > F ={res}    0.8604

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   114) ={res}    0.29
{txt}{col 13}Prob > F ={res}    0.7513

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       120
{txt}{hline 13}{c +}{hline 34}   F(9, 110)       = {res}     2.63
{txt}       Model {c |} {res} 30.1767803         9  3.35297558   {txt}Prob > F        ={res}    0.0087
{txt}    Residual {c |} {res} 140.494358       110  1.27722144   {txt}R-squared       ={res}    0.1768
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1095
{txt}       Total {c |} {res} 170.671139       119  1.43421125   {txt}Root MSE        =   {res} 1.1301

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .8627097{col 27}{space 2} .3108337{col 38}{space 1}    2.78{col 47}{space 3}0.006{col 55}{space 4} .2467103{col 68}{space 3} 1.478709
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1297266{col 27}{space 2} .0571744{col 38}{space 1}   -2.27{col 47}{space 3}0.025{col 55}{space 4}-.2430329{col 68}{space 3}-.0164203
{txt}{space 7}med_hs {c |}{col 15}{res}{space 2} .2402431{col 27}{space 2} .4740162{col 38}{space 1}    0.51{col 47}{space 3}0.613{col 55}{space 4}-.6991458{col 68}{space 3} 1.179632
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .2787942{col 27}{space 2} .4808809{col 38}{space 1}    0.58{col 47}{space 3}0.563{col 55}{space 4}-.6741989{col 68}{space 3} 1.231787
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .1174594{col 27}{space 2} .5004568{col 38}{space 1}    0.23{col 47}{space 3}0.815{col 55}{space 4}-.8743286{col 68}{space 3} 1.109247
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0063549{col 27}{space 2} .0171201{col 38}{space 1}   -0.37{col 47}{space 3}0.711{col 55}{space 4}-.0402828{col 68}{space 3} .0275731
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.7997912{col 27}{space 2} .2416184{col 38}{space 1}   -3.31{col 47}{space 3}0.001{col 55}{space 4}-1.278622{col 68}{space 3}-.3209602
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.2884648{col 27}{space 2} .2692979{col 38}{space 1}   -1.07{col 47}{space 3}0.286{col 55}{space 4}  -.82215{col 68}{space 3} .2452205
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0640745{col 27}{space 2} .1719559{col 38}{space 1}    0.37{col 47}{space 3}0.710{col 55}{space 4}-.2767017{col 68}{space 3} .4048507
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.074547{col 27}{space 2} .8688301{col 38}{space 1}    1.24{col 47}{space 3}0.219{col 55}{space 4}  -.64727{col 68}{space 3} 2.796365
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-6.283838{col 26}{space 2} 16.49106{col 37}{space 1}   -0.38{col 46}{space 3}0.703{col 54}{space 4}-38.60572{col 67}{space 3} 26.03805
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_hs = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} med_collplus = 0{p_end}

{txt}       F(  3,   110) ={res}    0.20
{txt}{col 13}Prob > F ={res}    0.8955

{p 0 7}{space 1}{text:( 1)}{space 1} med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   110) ={res}    0.27
{txt}{col 13}Prob > F ={res}    0.7625

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       120
{txt}{hline 13}{c +}{hline 34}   F(8, 111)       = {res}     2.94
{txt}       Model {c |} {res} 29.8486989         8  3.73108736   {txt}Prob > F        ={res}    0.0051
{txt}    Residual {c |} {res}  140.82244       111  1.26867063   {txt}R-squared       ={res}    0.1749
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1154
{txt}       Total {c |} {res} 170.671139       119  1.43421125   {txt}Root MSE        =   {res} 1.1264

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .8530876{col 27}{space 2} .3092131{col 38}{space 1}    2.76{col 47}{space 3}0.007{col 55}{space 4} .2403612{col 68}{space 3} 1.465814
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1318145{col 27}{space 2} .0568346{col 38}{space 1}   -2.32{col 47}{space 3}0.022{col 55}{space 4}-.2444361{col 68}{space 3} -.019193
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0741246{col 27}{space 2} .2602185{col 38}{space 1}    0.28{col 47}{space 3}0.776{col 55}{space 4}-.4415157{col 68}{space 3}  .589765
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} -.091415{col 27}{space 2} .2829692{col 38}{space 1}   -0.32{col 47}{space 3}0.747{col 55}{space 4}-.6521373{col 68}{space 3} .4693073
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0052448{col 27}{space 2} .0169224{col 38}{space 1}   -0.31{col 47}{space 3}0.757{col 55}{space 4}-.0387777{col 68}{space 3} .0282882
{txt}{space 6}m_white {c |}{col 15}{res}{space 2} -.788748{col 27}{space 2} .2398271{col 38}{space 1}   -3.29{col 47}{space 3}0.001{col 55}{space 4}-1.263981{col 68}{space 3}-.3135147
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.2890356{col 27}{space 2} .2683926{col 38}{space 1}   -1.08{col 47}{space 3}0.284{col 55}{space 4}-.8208734{col 68}{space 3} .2428023
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .0558544{col 27}{space 2} .1706153{col 38}{space 1}    0.33{col 47}{space 3}0.744{col 55}{space 4}-.2822311{col 68}{space 3}   .39394
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.280594{col 27}{space 2} .7652716{col 38}{space 1}    1.67{col 47}{space 3}0.097{col 55}{space 4} -.235843{col 68}{space 3}  2.79703
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-5.806776{col 26}{space 2} 14.32652{col 37}{space 1}   -0.41{col 46}{space 3}0.685{col 54}{space 4}-33.88624{col 67}{space 3} 22.27269
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   111) ={res}    0.17
{txt}{col 13}Prob > F ={res}    0.8404

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}        96
{txt}{hline 13}{c +}{hline 34}   F(9, 86)        = {res}     4.13
{txt}       Model {c |} {res} 37.7480559         9  4.19422844   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 87.3594317        86  1.01580735   {txt}R-squared       ={res}    0.3017
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2286
{txt}       Total {c |} {res} 125.107488        95  1.31692092   {txt}Root MSE        =   {res} 1.0079

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} 1.162639{col 27}{space 2} .3133295{col 38}{space 1}    3.71{col 47}{space 3}0.000{col 55}{space 4} .5397608{col 68}{space 3} 1.785518
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1582441{col 27}{space 2} .0577927{col 38}{space 1}   -2.74{col 47}{space 3}0.008{col 55}{space 4}-.2731323{col 68}{space 3} -.043356
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .3676327{col 27}{space 2} .2733413{col 38}{space 1}    1.34{col 47}{space 3}0.182{col 55}{space 4}-.1757518{col 68}{space 3} .9110173
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1160898{col 27}{space 2} .2917306{col 38}{space 1}   -0.40{col 47}{space 3}0.692{col 55}{space 4} -.696031{col 68}{space 3} .4638515
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}  .007564{col 27}{space 2} .0173755{col 38}{space 1}    0.44{col 47}{space 3}0.664{col 55}{space 4}-.0269775{col 68}{space 3} .0421054
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.8368176{col 27}{space 2} .2579044{col 38}{space 1}   -3.24{col 47}{space 3}0.002{col 55}{space 4}-1.349515{col 68}{space 3}-.3241206
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1844964{col 27}{space 2} .2801259{col 38}{space 1}    0.66{col 47}{space 3}0.512{col 55}{space 4}-.3723755{col 68}{space 3} .7413683
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1104231{col 27}{space 2}  .169682{col 38}{space 1}    0.65{col 47}{space 3}0.517{col 55}{space 4}-.2268935{col 68}{space 3} .4477398
{txt}{space 7}m_pc97 {c |}{col 15}{res}{space 2} .0004182{col 27}{space 2} .0089227{col 38}{space 1}    0.05{col 47}{space 3}0.963{col 55}{space 4}-.0173196{col 68}{space 3}  .018156
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .3529334{col 27}{space 2} 1.104315{col 38}{space 1}    0.32{col 47}{space 3}0.750{col 55}{space 4}-1.842372{col 68}{space 3} 2.548238
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}  7.14858{col 26}{space 2} 11.84543{col 37}{space 1}    0.60{col 46}{space 3}0.546{col 54}{space 4}-16.06804{col 67}{space 3}  30.3652
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,    86) ={res}    1.66
{txt}{col 13}Prob > F ={res}    0.1958

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}        75
{txt}{hline 13}{c +}{hline 34}   F(6, 68)        = {res}     2.20
{txt}       Model {c |} {res} 17.3872608         6  2.89787681   {txt}Prob > F        ={res}    0.0536
{txt}    Residual {c |} {res} 89.6560488        68  1.31847131   {txt}R-squared       ={res}    0.1624
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0885
{txt}       Total {c |} {res}  107.04331        74  1.44653121   {txt}Root MSE        =   {res} 1.1482

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .6134734{col 27}{space 2} .4121573{col 38}{space 1}    1.49{col 47}{space 3}0.141{col 55}{space 4}-.2089737{col 68}{space 3} 1.435921
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1466376{col 27}{space 2} .0758845{col 38}{space 1}   -1.93{col 47}{space 3}0.057{col 55}{space 4}-.2980627{col 68}{space 3} .0047875
{txt}{space 2}ln_wageFE_m {c |}{col 15}{res}{space 2} .1935418{col 27}{space 2}  .262935{col 38}{space 1}    0.74{col 47}{space 3}0.464{col 55}{space 4}-.3311369{col 68}{space 3} .7182204
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.7998303{col 27}{space 2} .2953863{col 38}{space 1}   -2.71{col 47}{space 3}0.009{col 55}{space 4}-1.389264{col 68}{space 3}-.2103961
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.7222226{col 27}{space 2} .3645102{col 38}{space 1}   -1.98{col 47}{space 3}0.052{col 55}{space 4}-1.449591{col 68}{space 3} .0051461
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1018891{col 27}{space 2} .1930387{col 38}{space 1}    0.53{col 47}{space 3}0.599{col 55}{space 4}-.2833136{col 68}{space 3} .4870918
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.472178{col 27}{space 2}  .938947{col 38}{space 1}    1.57{col 47}{space 3}0.122{col 55}{space 4}-.4014615{col 68}{space 3} 3.345818
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.587144{col 26}{space 2} 2.758699{col 37}{space 1}   -0.58{col 46}{space 3}0.565{col 54}{space 4}-6.994095{col 67}{space 3} 3.819806
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{bf}{err}med_hs{sf} not found
{bf}med_scoll{sf} not found
{txt}(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}ln_hhinvest {c |}{res}         94    2.379073    .9203381   .4307829   4.757396
{txt}(65 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}ln_hhinvest {c |}{res}        443    2.224198    .9042109    .080982   4.757396
{txt}{space 2}d_ln_hhinv {c |}{res}        443   -.1828469    1.067517  -2.713141   2.807864



OLS with nonlinear terms

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}        94
{txt}{hline 13}{c +}{hline 34}   F(9, 84)        = {res}     5.85
{txt}       Model {c |} {res} 49.5624618         9   5.5069402   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 79.1143144        84  .941837076   {txt}R-squared       ={res}    0.3852
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3193
{txt}       Total {c |} {res} 128.676776        93  1.38362125   {txt}Root MSE        =   {res} .97048

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .6559291{col 27}{space 2} .3034057{col 38}{space 1}    2.16{col 47}{space 3}0.033{col 55}{space 4} .0525735{col 68}{space 3} 1.259285
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0478966{col 27}{space 2} .0585217{col 38}{space 1}   -0.82{col 47}{space 3}0.415{col 55}{space 4}-.1642734{col 68}{space 3} .0684801
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} -.009564{col 27}{space 2} .2612145{col 38}{space 1}   -0.04{col 47}{space 3}0.971{col 55}{space 4}-.5290177{col 68}{space 3} .5098897
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1962572{col 27}{space 2} .2772429{col 38}{space 1}   -0.71{col 47}{space 3}0.481{col 55}{space 4}-.7475851{col 68}{space 3} .3550706
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} .0060391{col 27}{space 2} .0167813{col 38}{space 1}    0.36{col 47}{space 3}0.720{col 55}{space 4}-.0273324{col 68}{space 3} .0394106
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.7060157{col 27}{space 2} .2287473{col 38}{space 1}   -3.09{col 47}{space 3}0.003{col 55}{space 4}-1.160905{col 68}{space 3}-.2511266
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .0548582{col 27}{space 2} .2579437{col 38}{space 1}    0.21{col 47}{space 3}0.832{col 55}{space 4} -.458091{col 68}{space 3} .5678075
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1265601{col 27}{space 2} .1574666{col 38}{space 1}    0.80{col 47}{space 3}0.424{col 55}{space 4}-.1865795{col 68}{space 3} .4396997
{txt}{space 6}logterm {c |}{col 15}{res}{space 2} .5573082{col 27}{space 2} .1167241{col 38}{space 1}    4.77{col 47}{space 3}0.000{col 55}{space 4} .3251894{col 68}{space 3} .7894269
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-1.249967{col 27}{space 2} .8791634{col 38}{space 1}   -1.42{col 47}{space 3}0.159{col 55}{space 4}-2.998279{col 68}{space 3} .4983463
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.906377{col 26}{space 2} 2.562877{col 37}{space 1}   -0.74{col 46}{space 3}0.457{col 54}{space 4}-6.929524{col 67}{space 3} 3.116769
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logterm])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} 3.076053{col 26}{space 2} 3.043951{col 37}{space 1}    1.01{col 46}{space 3}0.312{col 54}{space 4}-2.889982{col 67}{space 3} 9.042088
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,    84) ={res}    0.33
{txt}{col 13}Prob > F ={res}    0.7211

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}        94
{txt}{hline 13}{c +}{hline 34}   F(11, 82)       = {res}     6.57
{txt}       Model {c |} {res} 60.2952486        11  5.48138623   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 68.3815276        82  .833921068   {txt}R-squared       ={res}    0.4686
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3973
{txt}       Total {c |} {res} 128.676776        93  1.38362125   {txt}Root MSE        =   {res} .91319

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .7368763{col 27}{space 2} .2868016{col 38}{space 1}    2.57{col 47}{space 3}0.012{col 55}{space 4} .1663364{col 68}{space 3} 1.307416
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.047433{col 27}{space 2}  .055693{col 38}{space 1}   -0.85{col 47}{space 3}0.397{col 55}{space 4}-.1582241{col 68}{space 3} .0633582
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .2596019{col 27}{space 2} .2592494{col 38}{space 1}    1.00{col 47}{space 3}0.320{col 55}{space 4}-.2561279{col 68}{space 3} .7753316
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .2912528{col 27}{space 2}  .295713{col 38}{space 1}    0.98{col 47}{space 3}0.328{col 55}{space 4}-.2970146{col 68}{space 3} .8795202
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}  .016271{col 27}{space 2} .0160484{col 38}{space 1}    1.01{col 47}{space 3}0.314{col 55}{space 4}-.0156544{col 68}{space 3} .0481965
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.4288898{col 27}{space 2} .2288315{col 38}{space 1}   -1.87{col 47}{space 3}0.064{col 55}{space 4}-.8841085{col 68}{space 3} .0263289
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2053681{col 27}{space 2}  .253692{col 38}{space 1}    0.81{col 47}{space 3}0.421{col 55}{space 4}-.2993062{col 68}{space 3} .7100424
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}-.0350603{col 27}{space 2} .1560594{col 38}{space 1}   -0.22{col 47}{space 3}0.823{col 55}{space 4}-.3455122{col 68}{space 3} .2753917
{txt}{space 6}logterm {c |}{col 15}{res}{space 2} .7160086{col 27}{space 2} .3840191{col 38}{space 1}    1.86{col 47}{space 3}0.066{col 55}{space 4}-.0479276{col 68}{space 3} 1.479945
{txt}{space 8}term2 {c |}{col 15}{res}{space 2} 16.85268{col 27}{space 2} 10.41676{col 38}{space 1}    1.62{col 47}{space 3}0.110{col 55}{space 4} -3.86957{col 68}{space 3} 37.57494
{txt}{space 3}d_ln_hhinv {c |}{col 15}{res}{space 2} -.461304{col 27}{space 2} .1494767{col 38}{space 1}   -3.09{col 47}{space 3}0.003{col 55}{space 4}-.7586606{col 68}{space 3}-.1639473
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-2.929708{col 27}{space 2} 1.696693{col 38}{space 1}   -1.73{col 47}{space 3}0.088{col 55}{space 4} -6.30497{col 68}{space 3} .4455542
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-2.800494{col 26}{space 2} 4.142492{col 37}{space 1}   -0.68{col 46}{space 3}0.499{col 54}{space 4}-10.91963{col 67}{space 3} 5.318641
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logterm])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} 1.627072{col 26}{space 2} 1.393897{col 37}{space 1}    1.17{col 46}{space 3}0.243{col 54}{space 4}-1.104915{col 67}{space 3} 4.359059
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,    82) ={res}    0.63
{txt}{col 13}Prob > F ={res}    0.5337

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       112
{txt}{hline 13}{c +}{hline 34}   F(9, 102)       = {res}     2.64
{txt}       Model {c |} {res} 27.4672422         9   3.0519158   {txt}Prob > F        ={res}    0.0087
{txt}    Residual {c |} {res} 117.980035       102  1.15666701   {txt}R-squared       ={res}    0.1888
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1173
{txt}       Total {c |} {res} 145.447277       111  1.31033583   {txt}Root MSE        =   {res} 1.0755

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .6448414{col 27}{space 2} .3114481{col 38}{space 1}    2.07{col 47}{space 3}0.041{col 55}{space 4} .0270856{col 68}{space 3} 1.262597
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0779703{col 27}{space 2} .0846232{col 38}{space 1}   -0.92{col 47}{space 3}0.359{col 55}{space 4}-.2458201{col 68}{space 3} .0898796
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0176831{col 27}{space 2} .2894994{col 38}{space 1}    0.06{col 47}{space 3}0.951{col 55}{space 4}-.5565377{col 68}{space 3} .5919038
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2003362{col 27}{space 2} .3168925{col 38}{space 1}   -0.63{col 47}{space 3}0.529{col 55}{space 4}-.8288909{col 68}{space 3} .4282186
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0021106{col 27}{space 2} .0167094{col 38}{space 1}   -0.13{col 47}{space 3}0.900{col 55}{space 4}-.0352535{col 68}{space 3} .0310323
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.6916184{col 27}{space 2} .2600515{col 38}{space 1}   -2.66{col 47}{space 3}0.009{col 55}{space 4}-1.207429{col 68}{space 3}-.1758075
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}-.1644156{col 27}{space 2} .2737307{col 38}{space 1}   -0.60{col 47}{space 3}0.549{col 55}{space 4}-.7073591{col 68}{space 3}  .378528
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1270684{col 27}{space 2} .1676704{col 38}{space 1}    0.76{col 47}{space 3}0.450{col 55}{space 4}-.2055051{col 68}{space 3} .4596419
{txt}{space 3}logtermPHI {c |}{col 15}{res}{space 2} .3628333{col 27}{space 2} .4112791{col 38}{space 1}    0.88{col 47}{space 3}0.380{col 55}{space 4}-.4529369{col 68}{space 3} 1.178603
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.0746437{col 27}{space 2} 1.713728{col 38}{space 1}   -0.04{col 47}{space 3}0.965{col 55}{space 4}-3.473816{col 68}{space 3} 3.324529
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.815643{col 26}{space 2} 2.469113{col 37}{space 1}   -0.74{col 46}{space 3}0.462{col 54}{space 4}-6.655015{col 67}{space 3} 3.023729
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logtermPHI])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} 84.02234{col 26}{space 2} 5053.569{col 37}{space 1}    0.02{col 46}{space 3}0.987{col 54}{space 4}-9820.791{col 67}{space 3} 9988.836
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   102) ={res}    0.34
{txt}{col 13}Prob > F ={res}    0.7114
{com}. 
. 
. 
. 
. *********** Two-Parent Households (2002, 2007) ***********
. 
. clear 
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. keep if curr_married==1
{txt}(556 observations deleted)
{com}. 
. * keep if prob_work_m >= `p_work_min_m' & prob_work_m !=. &  prob_work_f >= `p_work_min_f' & prob_work_f !=.  // comment/uncomment depending on if want to restrict sample to this with high probability of work
. 
. 
. 
. * First-stage PHI functions
. 
. local Zspec = "5"
.         
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel  `Zm`Zspec''        if prob_work_m >= `p_work_min_m' & prob_work_m !=.

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       491
{txt}{hline 13}{c +}{hline 34}   F(8, 482)       = {res}    14.47
{txt}       Model {c |} {res} 125.073579         8  15.6341974   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 520.860172       482  1.08062276   {txt}R-squared       ={res}    0.1936
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1802
{txt}       Total {c |} {res} 645.933751       490  1.31823214   {txt}Root MSE        =   {res} 1.0395

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2}  .627938{col 27}{space 2} .0794965{col 38}{space 1}    7.90{col 47}{space 3}0.000{col 55}{space 4} .4717355{col 68}{space 3} .7841405
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1316055{col 27}{space 2} .0260349{col 38}{space 1}   -5.05{col 47}{space 3}0.000{col 55}{space 4}-.1827614{col 68}{space 3}-.0804496
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.1243583{col 27}{space 2}  .127726{col 38}{space 1}   -0.97{col 47}{space 3}0.331{col 55}{space 4} -.375327{col 68}{space 3} .1266103
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2687168{col 27}{space 2} .1320329{col 38}{space 1}   -2.04{col 47}{space 3}0.042{col 55}{space 4}-.5281479{col 68}{space 3}-.0092857
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0053975{col 27}{space 2} .0091094{col 38}{space 1}   -0.59{col 47}{space 3}0.554{col 55}{space 4}-.0232966{col 68}{space 3} .0125016
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1699007{col 27}{space 2} .1072055{col 38}{space 1}   -1.58{col 47}{space 3}0.114{col 55}{space 4}-.3805485{col 68}{space 3} .0407472
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2910007{col 27}{space 2} .1474027{col 38}{space 1}    1.97{col 47}{space 3}0.049{col 55}{space 4} .0013694{col 68}{space 3}  .580632
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1068818{col 27}{space 2} .0761342{col 38}{space 1}    1.40{col 47}{space 3}0.161{col 55}{space 4}-.0427141{col 68}{space 3} .2564778
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.981708{col 27}{space 2} .4289885{col 38}{space 1}    4.62{col 47}{space 3}0.000{col 55}{space 4}  1.13879{col 68}{space 3} 2.824627
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. predict lnPHImm_ols`Zspec', xb      // predicted time relative to good expenditures, Phi_m, for married mothers
{txt}(218 missing values generated)
{com}. 
. regress ln_tau_f_g_exp_ratio  ln_wage_f_rel  `Zf`Zspec''        if prob_work_f >= `p_work_min_f' & prob_work_f !=.

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       582
{txt}{hline 13}{c +}{hline 34}   F(8, 573)       = {res}    13.00
{txt}       Model {c |} {res} 139.839212         8  17.4799015   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 770.302151       573  1.34433185   {txt}R-squared       ={res}    0.1536
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1418
{txt}       Total {c |} {res} 910.141364       581  1.56650837   {txt}Root MSE        =   {res} 1.1595

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_f_rel {c |}{col 15}{res}{space 2} .6778651{col 27}{space 2} .0896724{col 38}{space 1}    7.56{col 47}{space 3}0.000{col 55}{space 4} .5017385{col 68}{space 3} .8539917
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1073358{col 27}{space 2} .0265679{col 38}{space 1}   -4.04{col 47}{space 3}0.000{col 55}{space 4}-.1595182{col 68}{space 3}-.0551533
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2}-.1295592{col 27}{space 2} .1305496{col 38}{space 1}   -0.99{col 47}{space 3}0.321{col 55}{space 4}-.3859733{col 68}{space 3} .1268549
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2} .0710678{col 27}{space 2} .1267438{col 38}{space 1}    0.56{col 47}{space 3}0.575{col 55}{space 4}-.1778713{col 68}{space 3}  .320007
{txt}{space 8}f_age {c |}{col 15}{res}{space 2}-.0100924{col 27}{space 2} .0081044{col 38}{space 1}   -1.25{col 47}{space 3}0.214{col 55}{space 4}-.0260104{col 68}{space 3} .0058255
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0527072{col 27}{space 2} .1234486{col 38}{space 1}   -0.43{col 47}{space 3}0.670{col 55}{space 4}-.2951742{col 68}{space 3} .1897597
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1477398{col 27}{space 2} .1340448{col 38}{space 1}    1.10{col 47}{space 3}0.271{col 55}{space 4}-.1155393{col 68}{space 3} .4110189
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1682977{col 27}{space 2} .0795296{col 38}{space 1}    2.12{col 47}{space 3}0.035{col 55}{space 4} .0120926{col 68}{space 3} .3245029
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.281674{col 27}{space 2} .4343194{col 38}{space 1}    2.95{col 47}{space 3}0.003{col 55}{space 4} .4286219{col 68}{space 3} 2.134726
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. predict lnPHIf_ols`Zspec', xb       // predicted time relative to good expenditures, Phi_f, for fathers
{txt}(139 missing values generated)
{com}. 
. summ lnPHImm_ols`Zspec' lnPHIf_ols`Zspec' ln_tau_m_g_exp_ratio ln_tau_m_g_exp_ratio

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
lnPHImm_ols5 {c |}{res}        738    2.114136    .5424476  -1.470761    3.79224
{txt}{space 1}lnPHIf_ols5 {c |}{res}        817    2.182337    .4895023   .4573383   3.658396
{txt}ln_tau_m_g.. {c |}{res}        593     2.10133    1.155362  -2.218408   4.831703
{txt}ln_tau_m_g.. {c |}{res}        593     2.10133    1.155362  -2.218408   4.831703
{com}. 
. 
. 
. * Generate nonlinear terms for below
. 
. gen logtermPHI = ln(1.0 + exp(lnPHImm_ols`Zspec') + exp(lnPHIf_ols`Zspec'))
{txt}(310 missing values generated)
{com}.         
. gen Rf = exp(ln_tau_f_g_exp_ratio)
{txt}(354 missing values generated)
{com}. gen Rm = exp(ln_tau_m_g_exp_ratio)
{txt}(363 missing values generated)
{com}.                 
. gen temp    = 1.0 + Rm + Rf
{txt}(491 missing values generated)
{com}. gen logterm = ln(temp)
{txt}(491 missing values generated)
{com}. gen term_f   = Rf*(1.0 + Rm)/(2.0*temp*temp)    
{txt}(491 missing values generated)
{com}. gen term_m   = Rm*(1.0 + Rf)/(2.0*temp*temp)
{txt}(491 missing values generated)
{com}. gen term_g   = (Rf + Rm)/(2.0*temp*temp) 
{txt}(491 missing values generated)
{com}.                 
. summ Rf Rm logterm term_f term_m term_g 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 10}Rf {c |}{res}        602     18.7662    29.43284   .2992049   254.4821
{txt}{space 10}Rm {c |}{res}        593    14.82301    18.12842   .1087821   125.4244
{txt}{space 5}logterm {c |}{res}        465    3.048575    .9382626   .7455514   5.445988
{txt}{space 6}term_f {c |}{res}        465    .1030455    .0254483   .0047132   .1249998
{txt}{space 6}term_m {c |}{res}        465     .102997    .0249834   .0025839   .1249998
{txt}{hline 13}{c +}{hline 57}
{space 6}term_g {c |}{res}        465    .0309199    .0259341   .0021475   .1246742
{com}. corr logterm term_f term_m term_g 
{txt}(obs=465)

             {c |}  logterm   term_f   term_m   term_g
{hline 13}{c +}{hline 36}
     logterm {c |}{res}   1.0000
      {txt}term_f {c |}{res}  -0.0507   1.0000
      {txt}term_m {c |}{res}  -0.0558   0.7903   1.0000
      {txt}term_g {c |}{res}  -0.9218   0.0480   0.0490   1.0000

{com}. 
.  
. foreach pvar in 4ca{c -(}
{txt}  2{com}. 
.     disp  _n(3) "MARRIED COUPLES, CHILD CARE VS. GOODS" 
{txt}  3{com}.     disp  _n(1) "Price measure: `pvar'" _n(1)           
{txt}  4{com}.         
. * OLS estimates
. 
.         local zval= 5
{txt}  5{com}.                 regress ln_invratio_imp  ln_pratio_`pvar' `Z`zval''
{txt}  6{com}.                 nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt}  7{com}.                 local gam_M_ols`zval' = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt}  8{com}.                 capture noisily test med_scoll med_collplus
{txt}  9{com}.                 capture noisily test fed_scoll fed_collplus
{txt} 10{com}.                 estimates store est_M_ols`zval'
{txt} 11{com}.                 
.         
.         * get a de-meaned and re-scaled value of ln_hhinvest for relevant sample for regressor in Y vs. g relative demand equation -- (ln(g)-E[ln(g)])/Var(ln(g)) 
.                 
.         gen nonmiss_Yvg = (ln_invratio_imp !=. & ln_tau_m_g_exp_ratio !=. & ln_tau_f_g_exp_ratio !=. & ln_pratio_`pvar' !=. & ln_hhinv !=.)  // note that logterm and other nonlinear terms are functions of ln_tau_m_g_exp_ratio & ln_tau_m_g_exp_ratio
{txt} 12{com}.         foreach zvar in `Z5'{c -(}
{txt} 13{com}.                 replace nonmiss_Yvg=0 if `zvar'==.     // setting indicator to zero if variable is missing
{txt} 14{com}.         {c )-}
{txt} 15{com}.         
.         summ ln_hhinvest if (nonmiss_Yvg==1)             
{txt} 16{com}.         gen d_ln_hhinv = (ln_hhinvest - r(mean))/r(Var)   
{txt} 17{com}.         summ ln_hhinvest d_ln_hhinv     
{txt} 18{com}.         
.         
.         disp _n(3) "OLS with nonlinear terms"   
{txt} 19{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Z5' logterm                                // no input measurement error case
{txt} 20{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 21{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logterm])
{txt} 22{com}.         capture noisily test med_scoll med_collplus
{txt} 23{com}.         capture noisily test fed_scoll fed_collplus
{txt} 24{com}.         local gam_M_log = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 25{com}.         estimates store est_M_log
{txt} 26{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Z5' logterm term_f term_m term_g d_ln_hhinv  // includes terms for input measurement error case
{txt} 27{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 28{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logterm])
{txt} 29{com}.         capture noisily test med_scoll med_collplus
{txt} 30{com}.         capture noisily test fed_scoll fed_collplus
{txt} 31{com}.         local gam_M_ME = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 32{com}.         estimates store est_M_ME                
{txt} 33{com}.                 
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Z5' logtermPHI                            // includes predicted relative time/goods expenditure
{txt} 34{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 35{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logtermPHI])
{txt} 36{com}.         capture noisily test med_scoll med_collplus
{txt} 37{com}.         capture noisily test fed_scoll fed_collplus
{txt} 38{com}.         local gam_M_PHI = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 39{com}.         estimates store est_M_PHI
{txt} 40{com}.         
.         
.         drop d_ln_hhinv nonmiss_Yvg
{txt} 41{com}.         
. {c )-} // end loop over pvar



MARRIED COUPLES, CHILD CARE VS. GOODS

Price measure: 4ca


{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       227
{txt}{hline 13}{c +}{hline 34}   F(11, 215)      = {res}     2.95
{txt}       Model {c |} {res}  45.606124        11  4.14601127   {txt}Prob > F        ={res}    0.0012
{txt}    Residual {c |} {res} 302.359107       215  1.40632143   {txt}R-squared       ={res}    0.1311
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0866
{txt}       Total {c |} {res} 347.965231       226  1.53966916   {txt}Root MSE        =   {res} 1.1859

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .5889919{col 27}{space 2} .2754183{col 38}{space 1}    2.14{col 47}{space 3}0.034{col 55}{space 4} .0461261{col 68}{space 3} 1.131858
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1190149{col 27}{space 2} .0464715{col 38}{space 1}   -2.56{col 47}{space 3}0.011{col 55}{space 4} -.210613{col 68}{space 3}-.0274167
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} -.191158{col 27}{space 2} .2154495{col 38}{space 1}   -0.89{col 47}{space 3}0.376{col 55}{space 4}-.6158216{col 68}{space 3} .2335056
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.4959594{col 27}{space 2} .2191834{col 38}{space 1}   -2.26{col 47}{space 3}0.025{col 55}{space 4}-.9279829{col 68}{space 3} -.063936
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} .0070202{col 27}{space 2} .0237807{col 38}{space 1}    0.30{col 47}{space 3}0.768{col 55}{space 4}-.0398529{col 68}{space 3} .0538933
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2} .1275806{col 27}{space 2} .2172107{col 38}{space 1}    0.59{col 47}{space 3}0.558{col 55}{space 4}-.3005545{col 68}{space 3} .5557158
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2}-.3343103{col 27}{space 2} .2122127{col 38}{space 1}   -1.58{col 47}{space 3}0.117{col 55}{space 4}-.7525942{col 68}{space 3} .0839735
{txt}{space 8}f_age {c |}{col 15}{res}{space 2}-.0156351{col 27}{space 2} .0199841{col 38}{space 1}   -0.78{col 47}{space 3}0.435{col 55}{space 4} -.055025{col 68}{space 3} .0237548
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1010305{col 27}{space 2} .1771434{col 38}{space 1}   -0.57{col 47}{space 3}0.569{col 55}{space 4}-.4501907{col 68}{space 3} .2481296
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .0213032{col 27}{space 2} .1751372{col 38}{space 1}    0.12{col 47}{space 3}0.903{col 55}{space 4}-.3239025{col 68}{space 3}  .366509
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1688814{col 27}{space 2} .1511466{col 38}{space 1}    1.12{col 47}{space 3}0.265{col 55}{space 4}-.1290375{col 68}{space 3} .4668003
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.379868{col 27}{space 2} .7688377{col 38}{space 1}    1.79{col 47}{space 3}0.074{col 55}{space 4} -.135556{col 68}{space 3} 2.895293
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.433042{col 26}{space 2} 1.630392{col 37}{space 1}   -0.88{col 46}{space 3}0.379{col 54}{space 4}-4.628551{col 67}{space 3} 1.762467
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   215) ={res}    2.76
{txt}{col 13}Prob > F ={res}    0.0656

{p 0 7}{space 1}{text:( 1)}{space 1} fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_collplus = 0{p_end}

{txt}       F(  2,   215) ={res}    2.28
{txt}{col 13}Prob > F ={res}    0.1046
{txt}(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made)
(0 real changes made)
(0 real changes made)
(0 real changes made)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}ln_hhinvest {c |}{res}        155    2.561283    .8671776   .2972515   4.624218
{txt}(87 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}ln_hhinvest {c |}{res}        869    2.649527    .9159187   .1431008   5.029684
{txt}{space 2}d_ln_hhinv {c |}{res}        869    .1173452    1.217982  -3.215681    3.28246



OLS with nonlinear terms

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       155
{txt}{hline 13}{c +}{hline 34}   F(12, 142)      = {res}     4.54
{txt}       Model {c |} {res} 66.7778641        12  5.56482201   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 173.921295       142  1.22479785   {txt}R-squared       ={res}    0.2774
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2164
{txt}       Total {c |} {res} 240.699159       154  1.56298155   {txt}Root MSE        =   {res} 1.1067

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .4284331{col 27}{space 2} .3147026{col 38}{space 1}    1.36{col 47}{space 3}0.176{col 55}{space 4}-.1936745{col 68}{space 3} 1.050541
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1120636{col 27}{space 2} .0571441{col 38}{space 1}   -1.96{col 47}{space 3}0.052{col 55}{space 4}-.2250267{col 68}{space 3} .0008994
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0757704{col 27}{space 2} .2515203{col 38}{space 1}    0.30{col 47}{space 3}0.764{col 55}{space 4}-.4214377{col 68}{space 3} .5729785
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.0375373{col 27}{space 2} .2524951{col 38}{space 1}   -0.15{col 47}{space 3}0.882{col 55}{space 4}-.5366724{col 68}{space 3} .4615978
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0238545{col 27}{space 2} .0297868{col 38}{space 1}   -0.80{col 47}{space 3}0.425{col 55}{space 4}-.0827374{col 68}{space 3} .0350283
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2} .1153157{col 27}{space 2} .2530276{col 38}{space 1}    0.46{col 47}{space 3}0.649{col 55}{space 4} -.384872{col 68}{space 3} .6155034
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2}-.6767959{col 27}{space 2} .2421837{col 38}{space 1}   -2.79{col 47}{space 3}0.006{col 55}{space 4}-1.155547{col 68}{space 3}-.1980446
{txt}{space 8}f_age {c |}{col 15}{res}{space 2} .0169876{col 27}{space 2} .0244433{col 38}{space 1}    0.69{col 47}{space 3}0.488{col 55}{space 4}-.0313323{col 68}{space 3} .0653074
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.1065191{col 27}{space 2} .1984619{col 38}{space 1}   -0.54{col 47}{space 3}0.592{col 55}{space 4}-.4988407{col 68}{space 3} .2858025
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .2002866{col 27}{space 2} .2128518{col 38}{space 1}    0.94{col 47}{space 3}0.348{col 55}{space 4}-.2204811{col 68}{space 3} .6210544
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1033027{col 27}{space 2} .1705015{col 38}{space 1}    0.61{col 47}{space 3}0.546{col 55}{space 4}-.2337464{col 68}{space 3} .4403518
{txt}{space 6}logterm {c |}{col 15}{res}{space 2} .4649713{col 27}{space 2} .1062516{col 38}{space 1}    4.38{col 47}{space 3}0.000{col 55}{space 4}  .254932{col 68}{space 3} .6750105
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.2353437{col 27}{space 2} .9105992{col 38}{space 1}   -0.26{col 47}{space 3}0.796{col 55}{space 4}-2.035426{col 68}{space 3} 1.564739
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-.7495763{col 26}{space 2} .9633101{col 37}{space 1}   -0.78{col 46}{space 3}0.436{col 54}{space 4} -2.63763{col 67}{space 3} 1.138477
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logterm])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-4.019234{col 26}{space 2} 15.56384{col 37}{space 1}   -0.26{col 46}{space 3}0.796{col 54}{space 4}-34.52379{col 67}{space 3} 26.48532
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   142) ={res}    0.14
{txt}{col 13}Prob > F ={res}    0.8727

{p 0 7}{space 1}{text:( 1)}{space 1} fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_collplus = 0{p_end}

{txt}       F(  2,   142) ={res}    5.76
{txt}{col 13}Prob > F ={res}    0.0039

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       155
{txt}{hline 13}{c +}{hline 34}   F(16, 138)      = {res}     8.98
{txt}       Model {c |} {res} 122.763854        16  7.67274087   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 117.935305       138  .854603658   {txt}R-squared       ={res}    0.5100
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4532
{txt}       Total {c |} {res} 240.699159       154  1.56298155   {txt}Root MSE        =   {res} .92445

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .6087291{col 27}{space 2} .2653242{col 38}{space 1}    2.29{col 47}{space 3}0.023{col 55}{space 4} .0841025{col 68}{space 3} 1.133356
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0651217{col 27}{space 2}  .048457{col 38}{space 1}   -1.34{col 47}{space 3}0.181{col 55}{space 4}-.1609358{col 68}{space 3} .0306924
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .1455443{col 27}{space 2} .2143493{col 38}{space 1}    0.68{col 47}{space 3}0.498{col 55}{space 4}-.2782892{col 68}{space 3} .5693779
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2} .4216998{col 27}{space 2} .2218023{col 38}{space 1}    1.90{col 47}{space 3}0.059{col 55}{space 4}-.0168706{col 68}{space 3} .8602702
{txt}{space 8}m_age {c |}{col 15}{res}{space 2}-.0144472{col 27}{space 2}  .025295{col 38}{space 1}   -0.57{col 47}{space 3}0.569{col 55}{space 4}-.0644631{col 68}{space 3} .0355687
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2} .1014483{col 27}{space 2} .2117127{col 38}{space 1}    0.48{col 47}{space 3}0.633{col 55}{space 4}-.3171721{col 68}{space 3} .5200687
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2}-.2081214{col 27}{space 2} .2108181{col 38}{space 1}   -0.99{col 47}{space 3}0.325{col 55}{space 4}-.6249728{col 68}{space 3}   .20873
{txt}{space 8}f_age {c |}{col 15}{res}{space 2} .0060509{col 27}{space 2} .0207103{col 38}{space 1}    0.29{col 47}{space 3}0.771{col 55}{space 4}-.0348997{col 68}{space 3} .0470015
{txt}{space 6}m_white {c |}{col 15}{res}{space 2} .0863767{col 27}{space 2} .1682497{col 38}{space 1}    0.51{col 47}{space 3}0.609{col 55}{space 4} -.246304{col 68}{space 3} .4190574
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .3571343{col 27}{space 2} .1812316{col 38}{space 1}    1.97{col 47}{space 3}0.051{col 55}{space 4}-.0012155{col 68}{space 3} .7154842
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}-.0727627{col 27}{space 2} .1445422{col 38}{space 1}   -0.50{col 47}{space 3}0.615{col 55}{space 4}-.3585666{col 68}{space 3} .2130412
{txt}{space 6}logterm {c |}{col 15}{res}{space 2} -.131238{col 27}{space 2}  .223767{col 38}{space 1}   -0.59{col 47}{space 3}0.559{col 55}{space 4}-.5736933{col 68}{space 3} .3112172
{txt}{space 7}term_f {c |}{col 15}{res}{space 2} 8.014522{col 27}{space 2} 5.819395{col 38}{space 1}    1.38{col 47}{space 3}0.171{col 55}{space 4}-3.492189{col 68}{space 3} 19.52123
{txt}{space 7}term_m {c |}{col 15}{res}{space 2}-13.71005{col 27}{space 2} 6.630211{col 38}{space 1}   -2.07{col 47}{space 3}0.041{col 55}{space 4}-26.81999{col 68}{space 3}-.6001136
{txt}{space 7}term_g {c |}{col 15}{res}{space 2} .6077285{col 27}{space 2} 8.410633{col 38}{space 1}    0.07{col 47}{space 3}0.943{col 55}{space 4}-16.02264{col 68}{space 3}  17.2381
{txt}{space 3}d_ln_hhinv {c |}{col 15}{res}{space 2}-.7647787{col 27}{space 2} .1001445{col 38}{space 1}   -7.64{col 47}{space 3}0.000{col 55}{space 4}-.9627948{col 68}{space 3}-.5667627
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.570264{col 27}{space 2} 1.166311{col 38}{space 1}    1.35{col 47}{space 3}0.180{col 55}{space 4} -.735887{col 68}{space 3} 3.876416
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.555774{col 26}{space 2} 1.733093{col 37}{space 1}   -0.90{col 46}{space 3}0.369{col 54}{space 4}-4.952573{col 67}{space 3} 1.841025
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logterm])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.165012{col 26}{space 2} 1.212184{col 37}{space 1}   -0.96{col 46}{space 3}0.337{col 54}{space 4}-3.540848{col 67}{space 3} 1.210824
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   138) ={res}    2.00
{txt}{col 13}Prob > F ={res}    0.1392

{p 0 7}{space 1}{text:( 1)}{space 1} fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_collplus = 0{p_end}

{txt}       F(  2,   138) ={res}    0.99
{txt}{col 13}Prob > F ={res}    0.3727

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       198
{txt}{hline 13}{c +}{hline 34}   F(12, 185)      = {res}     2.31
{txt}       Model {c |} {res} 38.5323664        12  3.21103054   {txt}Prob > F        ={res}    0.0091
{txt}    Residual {c |} {res} 257.149194       185  1.38999565   {txt}R-squared       ={res}    0.1303
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0739
{txt}       Total {c |} {res} 295.681561       197  1.50092163   {txt}Root MSE        =   {res}  1.179

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invratio~p{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_pratio_4ca {c |}{col 15}{res}{space 2} .4638243{col 27}{space 2} .2928482{col 38}{space 1}    1.58{col 47}{space 3}0.115{col 55}{space 4}-.1139271{col 68}{space 3} 1.041576
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0964861{col 27}{space 2} .0657674{col 38}{space 1}   -1.47{col 47}{space 3}0.144{col 55}{space 4}-.2262367{col 68}{space 3} .0332644
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2}-.0618336{col 27}{space 2} .2266703{col 38}{space 1}   -0.27{col 47}{space 3}0.785{col 55}{space 4}-.5090246{col 68}{space 3} .3853573
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.2910955{col 27}{space 2}  .231577{col 38}{space 1}   -1.26{col 47}{space 3}0.210{col 55}{space 4}-.7479667{col 68}{space 3} .1657757
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} .0015866{col 27}{space 2} .0256765{col 38}{space 1}    0.06{col 47}{space 3}0.951{col 55}{space 4}-.0490698{col 68}{space 3}  .052243
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2} .1951558{col 27}{space 2} .2276111{col 38}{space 1}    0.86{col 47}{space 3}0.392{col 55}{space 4}-.2538914{col 68}{space 3} .6442029
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2}-.4474659{col 27}{space 2} .2469127{col 38}{space 1}   -1.81{col 47}{space 3}0.072{col 55}{space 4}-.9345925{col 68}{space 3} .0396607
{txt}{space 8}f_age {c |}{col 15}{res}{space 2}-.0054976{col 27}{space 2} .0214714{col 38}{space 1}   -0.26{col 47}{space 3}0.798{col 55}{space 4}-.0478578{col 68}{space 3} .0368626
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0146402{col 27}{space 2} .1890712{col 38}{space 1}   -0.08{col 47}{space 3}0.938{col 55}{space 4}-.3876531{col 68}{space 3} .3583727
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2}  .081052{col 27}{space 2} .2163944{col 38}{space 1}    0.37{col 47}{space 3}0.708{col 55}{space 4} -.345866{col 68}{space 3}   .50797
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1152262{col 27}{space 2} .1665238{col 38}{space 1}    0.69{col 47}{space 3}0.490{col 55}{space 4}-.2133035{col 68}{space 3}  .443756
{txt}{space 3}logtermPHI {c |}{col 15}{res}{space 2} .3114066{col 27}{space 2} .4098263{col 38}{space 1}    0.76{col 47}{space 3}0.448{col 55}{space 4}-.4971273{col 68}{space 3} 1.119941
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .1402014{col 27}{space 2} 1.710913{col 38}{space 1}    0.08{col 47}{space 3}0.935{col 55}{space 4}-3.235207{col 68}{space 3}  3.51561
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-.8650602{col 26}{space 2} 1.018657{col 37}{space 1}   -0.85{col 46}{space 3}0.396{col 54}{space 4}-2.861592{col 67}{space 3} 1.131472
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logtermPHI])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.063559{col 26}{space 2} 5.287944{col 37}{space 1}   -0.39{col 46}{space 3}0.696{col 54}{space 4}-12.42774{col 67}{space 3} 8.300622
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   185) ={res}    0.96
{txt}{col 13}Prob > F ={res}    0.3829

{p 0 7}{space 1}{text:( 1)}{space 1} fed_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_collplus = 0{p_end}

{txt}       F(  2,   185) ={res}    3.08
{txt}{col 13}Prob > F ={res}    0.0485
{com}. 
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-15: OLS estimates for child care/goods relative demand
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
. esttab estS_ols5 estS_log estS_PHI estS_ME est_M_ols5 est_M_log est_M_PHI est_M_ME  using "$tables/Sing_Mar_YPvpg_OLS.tex", coeflab(ln_pratio_`pvar' "$\ln(\tilde{c -(}P{c )-}_c)$"  age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"   fed_hs "Father HS grad"  fed_scoll "Father some coll."  fed_collplus "Father coll+" f_age "Father's age" m_age "Mother's age" m_white "Mother white" m_pc97 "Mother's cogn. score" num_child "Num of children" num_0_5 "Num children age 0-5" logterm "$\ln(1+R_m+R_f)$" logtermPHI "$\ln(1+e^{c -(}\Phi_m{c )-}+e^{c -(}\Phi_f{c )-})$" d_ln_hhinv "$-\sigma^2_g$")  stats(r2 N, fmt(3 0))  ti(OLS estimates for child care/goods relative demand (4ca, 1-2 kids ages 0-12, 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(40) modelwidth(10) wrap style(tex) mtitles("Single" "Single" "Single" "Single" "Married" "Married" "Married" "Married")  replace 
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/Sing_Mar_YPvpg_OLS.tex"'})
{com}. 
. 
. 
. ***** ALL Households (2002, 2007) *****
. 
. 
. clear 
. use "$temp/allfam_tmp.dta"
. 
. keep if year==2002 | year==2007
{txt}(1,250 observations deleted)
{com}. 
. * keep if prob_work_m >= `p_work_min_m' & prob_work_m !=. &  prob_work_f >= `p_work_min_f' & prob_work_f !=.  // comment/uncomment depending on if want to restrict sample to this with high probability of work
. 
. 
. * First-stage PHI functions
. 
. local Zspec = "5"
. 
. 
. regress ln_tau_m_g_exp_ratio  ln_wage_m_rel curr_married  `Zm`Zspec''   if prob_work_m >= `p_work_min_m' & prob_work_m !=.

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       727
{txt}{hline 13}{c +}{hline 34}   F(9, 717)       = {res}    18.64
{txt}       Model {c |} {res} 193.480948         9  21.4978831   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 826.887232       717  1.15325974   {txt}R-squared       ={res}    0.1896
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1794
{txt}       Total {c |} {res} 1020.36818       726  1.40546581   {txt}Root MSE        =   {res} 1.0739

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_m_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_m_rel {c |}{col 15}{res}{space 2} .6461114{col 27}{space 2}  .071264{col 38}{space 1}    9.07{col 47}{space 3}0.000{col 55}{space 4} .5062003{col 68}{space 3} .7860226
{txt}{space 1}curr_married {c |}{col 15}{res}{space 2}-.0737061{col 27}{space 2} .0949495{col 38}{space 1}   -0.78{col 47}{space 3}0.438{col 55}{space 4}-.2601183{col 68}{space 3} .1127061
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1410849{col 27}{space 2} .0222499{col 38}{space 1}   -6.34{col 47}{space 3}0.000{col 55}{space 4}-.1847677{col 68}{space 3}-.0974021
{txt}{space 4}med_scoll {c |}{col 15}{res}{space 2} .0110723{col 27}{space 2} .1021967{col 38}{space 1}    0.11{col 47}{space 3}0.914{col 55}{space 4}-.1895683{col 68}{space 3} .2117129
{txt}{space 1}med_collplus {c |}{col 15}{res}{space 2}-.1571411{col 27}{space 2} .1121116{col 38}{space 1}   -1.40{col 47}{space 3}0.161{col 55}{space 4}-.3772474{col 68}{space 3} .0629652
{txt}{space 8}m_age {c |}{col 15}{res}{space 2} -.007913{col 27}{space 2} .0075422{col 38}{space 1}   -1.05{col 47}{space 3}0.294{col 55}{space 4}-.0227205{col 68}{space 3} .0068944
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.2426265{col 27}{space 2} .0894697{col 38}{space 1}   -2.71{col 47}{space 3}0.007{col 55}{space 4}-.4182804{col 68}{space 3}-.0669726
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1582784{col 27}{space 2} .1254418{col 38}{space 1}    1.26{col 47}{space 3}0.207{col 55}{space 4}-.0879988{col 68}{space 3} .4045556
{txt}{space 4}num_child {c |}{col 15}{res}{space 2}  .089088{col 27}{space 2}  .061517{col 38}{space 1}    1.45{col 47}{space 3}0.148{col 55}{space 4}-.0316869{col 68}{space 3} .2098629
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.213075{col 27}{space 2} .3551268{col 38}{space 1}    6.23{col 47}{space 3}0.000{col 55}{space 4} 1.515863{col 68}{space 3} 2.910288
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. predict lnPHIm_ols`Zspec', xb      // predicted time relative to good expenditures, Phi_m, for all mothers
{txt}(403 missing values generated)
{com}. 
. regress ln_tau_f_g_exp_ratio  ln_wage_f_rel  `Zf`Zspec''        if prob_work_f >= `p_work_min_f' & prob_work_f !=.

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       582
{txt}{hline 13}{c +}{hline 34}   F(8, 573)       = {res}    13.00
{txt}       Model {c |} {res} 139.839212         8  17.4799015   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 770.302151       573  1.34433185   {txt}R-squared       ={res}    0.1536
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1418
{txt}       Total {c |} {res} 910.141364       581  1.56650837   {txt}Root MSE        =   {res} 1.1595

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_tau_f_g_e~{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_wage_f_rel {c |}{col 15}{res}{space 2} .6778651{col 27}{space 2} .0896724{col 38}{space 1}    7.56{col 47}{space 3}0.000{col 55}{space 4} .5017385{col 68}{space 3} .8539917
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.1073358{col 27}{space 2} .0265679{col 38}{space 1}   -4.04{col 47}{space 3}0.000{col 55}{space 4}-.1595182{col 68}{space 3}-.0551533
{txt}{space 4}fed_scoll {c |}{col 15}{res}{space 2}-.1295592{col 27}{space 2} .1305496{col 38}{space 1}   -0.99{col 47}{space 3}0.321{col 55}{space 4}-.3859733{col 68}{space 3} .1268549
{txt}{space 1}fed_collplus {c |}{col 15}{res}{space 2} .0710678{col 27}{space 2} .1267438{col 38}{space 1}    0.56{col 47}{space 3}0.575{col 55}{space 4}-.1778713{col 68}{space 3}  .320007
{txt}{space 8}f_age {c |}{col 15}{res}{space 2}-.0100924{col 27}{space 2} .0081044{col 38}{space 1}   -1.25{col 47}{space 3}0.214{col 55}{space 4}-.0260104{col 68}{space 3} .0058255
{txt}{space 6}m_white {c |}{col 15}{res}{space 2}-.0527072{col 27}{space 2} .1234486{col 38}{space 1}   -0.43{col 47}{space 3}0.670{col 55}{space 4}-.2951742{col 68}{space 3} .1897597
{txt}{space 6}num_0_5 {c |}{col 15}{res}{space 2} .1477398{col 27}{space 2} .1340448{col 38}{space 1}    1.10{col 47}{space 3}0.271{col 55}{space 4}-.1155393{col 68}{space 3} .4110189
{txt}{space 4}num_child {c |}{col 15}{res}{space 2} .1682977{col 27}{space 2} .0795296{col 38}{space 1}    2.12{col 47}{space 3}0.035{col 55}{space 4} .0120926{col 68}{space 3} .3245029
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.281674{col 27}{space 2} .4343194{col 38}{space 1}    2.95{col 47}{space 3}0.003{col 55}{space 4} .4286219{col 68}{space 3} 2.134726
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. predict lnPHIf_ols`Zspec', xb       // predicted time relative to good expenditures, Phi_f, for fathers
{txt}(695 missing values generated)
{com}. 
. summ lnPHIm_ols`Zspec' lnPHIf_ols`Zspec' ln_tau_m_g_exp_ratio ln_tau_m_g_exp_ratio

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}lnPHIm_ols5 {c |}{res}      1,109    2.133349    .5448666   -1.59126   3.722854
{txt}{space 1}lnPHIf_ols5 {c |}{res}        817    2.182337    .4895023   .4573383   3.658396
{txt}ln_tau_m_g.. {c |}{res}        860    2.129995     1.18899  -2.867899   4.831703
{txt}ln_tau_m_g.. {c |}{res}        860    2.129995     1.18899  -2.867899   4.831703
{com}. 
. 
. 
. * Generate nonlinear terms for below
. 
. gen logtermPHI = ln(1.0 + exp(lnPHIm_ols`Zspec') + exp(lnPHIf_ols`Zspec'))
{txt}(866 missing values generated)
{com}. replace logtermPHI = ln(1.0 + exp(lnPHIm_ols`Zspec')) if curr_married==0
{txt}(371 real changes made)
{com}.         
. gen Rf = exp(ln_tau_f_g_exp_ratio)
{txt}(910 missing values generated)
{com}. gen Rm = exp(ln_tau_m_g_exp_ratio)
{txt}(652 missing values generated)
{com}.                 
. gen temp    = 1.0 + Rm + Rf
{txt}(1,047 missing values generated)
{com}. replace temp = 1.0 + Rm   if curr_married==0
{txt}(267 real changes made)
{com}. 
. gen logterm = ln(temp)
{txt}(780 missing values generated)
{com}. 
. gen term_f   = Rf*(1.0 + Rm)/(2.0*temp*temp)
{txt}(1,047 missing values generated)
{com}. gen term_m   = Rm*(1.0 + Rf)/(2.0*temp*temp)
{txt}(1,047 missing values generated)
{com}. gen term_g   = (Rf + Rm)/(2.0*temp*temp) 
{txt}(1,047 missing values generated)
{com}.                 
. replace term_f = 0 if curr_married==0
{txt}(508 real changes made)
{com}. replace term_m = Rm/(2.0*temp*temp) if curr_married==0
{txt}(267 real changes made)
{com}. replace term_g = 0 if curr_married==0
{txt}(508 real changes made)
{com}. 
. summ curr_married logtermPHI Rf Rm logterm term_f term_m term_g 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
curr_married {c |}{res}      1,464    .6530055    .4761767          0          1
{txt}{space 2}logtermPHI {c |}{res}      1,017    2.699856     .506791   .5054242   4.153788
{txt}{space 10}Rf {c |}{res}        602     18.7662    29.43284   .2992049   254.4821
{txt}{space 10}Rm {c |}{res}        860    15.70424    19.44012   .0568182   125.4244
{txt}{space 5}logterm {c |}{res}        732    2.806657    1.028269   .0552627   5.445988
{txt}{hline 13}{c +}{hline 57}
{space 6}term_f {c |}{res}        973    .0492458    .0544177          0   .1249998
{txt}{space 6}term_m {c |}{res}        732    .0842757    .0382366   .0025839   .1249998
{txt}{space 6}term_g {c |}{res}        973    .0147767    .0236612          0   .1246742
{com}. corr logterm term_f term_m term_g 
{txt}(obs=732)

             {c |}  logterm   term_f   term_m   term_g
{hline 13}{c +}{hline 36}
     logterm {c |}{res}   1.0000
      {txt}term_f {c |}{res}   0.2737   1.0000
      {txt}term_m {c |}{res}  -0.1405   0.7541   1.0000
      {txt}term_g {c |}{res}  -0.3620   0.5561   0.3988   1.0000

{com}. 
. 
.  
. foreach pvar in 4ca {c -(}
{txt}  2{com}. 
.     disp  _n(3) "ALL HH, CHILD CARE VS. GOODS" 
{txt}  3{com}.     disp  _n(1) "Price measure: `pvar'" _n(1)           
{txt}  4{com}.         
. * OLS estimates
. 
. 
. local zval= 5 
{txt}  5{com}.                 regress ln_invratio_imp  ln_pratio_`pvar' `Zall`zval''
{txt}  6{com}.                 nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt}  7{com}.                 local gam_ols`zval' = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt}  8{com}.                 capture noisily test med_scoll med_collplus
{txt}  9{com}.                 capture noisily test fed_all_scoll fed_all_collplus
{txt} 10{com}.                 estimates store est_ols`zval'
{txt} 11{com}.                 
.                 local rss_ols`zval'=e(rss)
{txt} 12{com}.                 local n_ols`zval'=e(N)
{txt} 13{com}. 
. 
.                 regress ln_invratio_imp  ln_pratio_`pvar' `Zall`zval'' SR
{txt} 14{com}.                 nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 15{com}.                 local gam_ols_SR_`zval' = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 16{com}.                 capture noisily test med_scoll med_collplus
{txt} 17{com}.                 capture noisily test fed_all_scoll fed_all_collplus
{txt} 18{com}.                 estimates store est_ols_SR_`zval'
{txt} 19{com}. 
.         
.         * get a de-meaned and re-scaled value of ln_hhinvest for relevant sample for regressor in Y vs. g relative demand equation -- (ln(g)-E[ln(g)])/Var(ln(g)) 
.                 
.         gen nonmiss_Yvg = (ln_invratio_imp !=. & temp !=. & ln_pratio_`pvar' !=. & ln_hhinv !=.)  // note that nonlinear terms are missing if temp is missing
{txt} 20{com}.         foreach zvar in `Zall5'{c -(}
{txt} 21{com}.                 replace nonmiss_Yvg=0 if `zvar'==.     // setting indicator to zero if variable is missing
{txt} 22{com}.         {c )-}
{txt} 23{com}.         
.         summ ln_hhinvest if (nonmiss_Yvg==1)              // NEW  
{txt} 24{com}.         gen d_ln_hhinv = (ln_hhinvest - r(mean))/r(Var)   // NEW  
{txt} 25{com}.         summ ln_hhinvest d_ln_hhinv     
{txt} 26{com}.         
.         
.         disp _n(3) "OLS with nonlinear terms -- without staff ratio"    
{txt} 27{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' logterm                                // no input measurement error case
{txt} 28{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 29{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logterm])
{txt} 30{com}.         capture noisily test med_scoll med_collplus
{txt} 31{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt} 32{com}.         local gam_log = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 33{com}.         estimates store est_log
{txt} 34{com}.         local rss_log=e(rss)
{txt} 35{com}.         local n_log=e(N)
{txt} 36{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' logterm term_f term_m term_g d_ln_hhinv  // includes terms for input measurement error case
{txt} 37{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 38{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logterm])
{txt} 39{com}.         capture noisily test med_scoll med_collplus
{txt} 40{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt} 41{com}.         local gam_ME = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 42{com}.         estimates store est_ME          
{txt} 43{com}.                 
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' logtermPHI                            // includes predicted relative time/goods expenditure
{txt} 44{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 45{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logtermPHI])
{txt} 46{com}.         capture noisily test med_scoll med_collplus
{txt} 47{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt} 48{com}.         local gam_PHI = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 49{com}.         estimates store est_PHI
{txt} 50{com}.         local rss_PHI=e(rss)
{txt} 51{com}.         local n_PHI=e(N)
{txt} 52{com}.         
.         
.         disp _n(3) "OLS with nonlinear terms -- with staff ratio"       
{txt} 53{com}.         
.         replace nonmiss_Yvg = 0 if SR==.   // set indicator to zero if staff ratio measure is missing
{txt} 54{com}.         summ ln_hhinvest if (nonmiss_Yvg==1)              
{txt} 55{com}.         replace d_ln_hhinv = (ln_hhinvest - r(mean))/r(Var)   
{txt} 56{com}.         summ ln_hhinvest d_ln_hhinv     
{txt} 57{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' SR logterm                                // no input measurement error case
{txt} 58{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 59{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logterm])
{txt} 60{com}.         capture noisily test med_scoll med_collplus
{txt} 61{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt} 62{com}.         local gam_SR_log = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 63{com}.         estimates store est_SR_log
{txt} 64{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' SR logterm term_f term_m term_g d_ln_hhinv  // includes terms for input measurement error case
{txt} 65{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 66{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logterm])
{txt} 67{com}.         capture noisily test med_scoll med_collplus
{txt} 68{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt} 69{com}.         local gam_SR_ME = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 70{com}.         estimates store est_SR_ME               
{txt} 71{com}.                 
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' SR logtermPHI                            // includes predicted relative time/goods expenditure
{txt} 72{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt} 73{com}.         nlcom rho: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0 + _b[logtermPHI])
{txt} 74{com}.         capture noisily test med_scoll med_collplus
{txt} 75{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt} 76{com}.         local gam_SR_PHI = _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 77{com}.         estimates store est_SR_PHI
{txt} 78{com}. 
.         
.         
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> ***  Table 4: OLS estimates for child care/goods relative demand, all families
. **Note: Columns 1-4 correspond to Table 4, Column 5 corresponds to Table E-1
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
.         esttab est_ols5 est_ols_SR_5  est_log est_PHI est_ME   using "$tables/All_YPvpg_`pvar'_OLS.tex", coeflab(ln_pratio_`pvar' "$\ln(\tilde{c -(}P{c )-}_{c -(}c,i{c )-})$" curr_married "Married" age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"   fed_all_hs "Marr x Father HS grad"  fed_all_scoll "Marr x Father some coll."  fed_all_collplus "Marr x Father coll+" f_all_age "Marr x Father's age" m_age "Mother's age" m_white "Mother white" m_pc97 "Mother's cogn. score" num_child "Num of children" num_0_5 "Num children age 0-5" SR "staff ratio" logterm "$\ln(1+R_m + Marr\cdot R_f)$" logtermPHI "$\ln(1+e^{c -(}\Phi_m{c )-}+Marr\cdot e^{c -(}\Phi_f{c )-})$" d_ln_hhinv "$-\sigma^2_g$")  stats(r2 rss N, fmt(3 3 0))  ti(OLS estimates for child care/goods relative demand (ALL, `pvar', 1-2 kids ages 0-12, 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(43) modelwidth(10) wrap style(tex) mlabels(,none)  replace 
{txt} 79{com}.         
. 
. 
.         **** Heckman selection corrected models for positive childcare expenditures
.         
.         
.         disp _n(3) "HECKMAN TWO-STEP Estimates" _n(2)
{txt} 80{com}.         
. 
.         heckman ln_invratio_imp  ln_pratio_`pvar' `Zall5', select(`pccare'  = ln_pratio_`pvar' `Zall5' i.year  ind_same_state live_grand old_child old_child_2plus) twostep
{txt} 81{com}.         estimates store heck_old
{txt} 82{com}.         
.         heckman ln_invratio_imp  ln_pratio_`pvar' `Zall5', select(`pccare'  = ln_pratio_`pvar' `Zall5' i.year  ind_same_state older_relative_hh old_child old_child_2plus) twostep
{txt} 83{com}.         estimates store heck_base
{txt} 84{com}.         
.         
.         heckman ln_invratio_imp  ln_pratio_`pvar' `Zall5', select(`pccare'  = ln_pratio_`pvar' `Zall5' i.year ind_same_state old_child old_child_2plus) twostep
{txt} 85{com}.         estimates store heck_base2
{txt} 86{com}.         
.         
.         heckman ln_invratio_imp  ln_pratio_`pvar' `Zall5' logtermPHI, select(`pccare'  = ln_pratio_`pvar' `Zall5' i.year  ind_same_state old_child old_child_2plus) twostep
{txt} 87{com}.         estimates store heck_PHI
{txt} 88{com}.         
.         heckman ln_invratio_imp  ln_pratio_`pvar' `Zall5' logtermPHI, select(`pccare'  = ln_pratio_`pvar' `Zall5' i.year logtermPHI ind_same_state old_child old_child_2plus) twostep
{txt} 89{com}.         estimates store heck_PHI2
{txt} 90{com}.         
. 
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> ***  Appendix  Table G-14: Heckman two-step estimates for childcare/goods relative demand
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
.         esttab  heck_base heck_base2 heck_PHI2 using "$tables/All_YPvpg_`pvar'_OLS_Heckman.tex", coeflab(ln_pratio_`pvar' "$\ln(\tilde{c -(}P{c )-}_{c -(}c,i{c )-})$" curr_married "Married" age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"   fed_all_hs "Marr x Father HS grad"  fed_all_scoll "Marr x Father some coll."  fed_all_collplus "Marr x Father coll+" f_all_age "Marr x Father's age" m_age "Mother's age" m_white "Mother white" num_child "Num of children" num_0_5 "Num children age 0-5" logtermPHI "$\ln(1+e^{c -(}\Phi_m{c )-}+Marr\cdot e^{c -(}\Phi_f{c )-})$" ind_same_state "HH Head same state" live_grand "Live w/grandparents" older_relative_hh "live w/older relative" old_child "Any children ages 13+" old_child_2plus "2+ children ages 13+") stats(N_selected N, fmt(0 0))  ti(Appendix Table: Heckman two-step estimates for childcare/goods relative demand (ALL, `pvar', 1-2 kids ages 0-12, 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(44) modelwidth(15) wrap style(tex)  mlabels(,none)  replace
{txt} 91{com}.         
.         
.         
.         **** Estimate separate models by child age
.         
.         regress ln_invratio_imp  ln_pratio_`pvar' `Zall5' if age >=5 & age <=8
{txt} 92{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 93{com}.         capture noisily test med_scoll med_collplus
{txt} 94{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt} 95{com}.         estimates store est_ols5_5_8
{txt} 96{com}.         local rss_ols5_5_8=e(rss)
{txt} 97{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar' `Zall5' if age >=9 & age <=12
{txt} 98{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt} 99{com}.         capture noisily test med_scoll med_collplus
{txt}100{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt}101{com}.         estimates store est_ols5_9_12   
{txt}102{com}.         local rss_ols5_9_12=e(rss)
{txt}103{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' logterm   if age >=5 & age <=8    // no input measurement error case
{txt}104{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt}105{com}.         capture noisily test med_scoll med_collplus
{txt}106{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt}107{com}.         estimates store est_log_5_8     
{txt}108{com}.         local rss_log_5_8=e(rss)
{txt}109{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' logterm   if age >=9 & age <=12    // no input measurement error case
{txt}110{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)   
{txt}111{com}.         capture noisily test med_scoll med_collplus
{txt}112{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt}113{com}.         estimates store est_log_9_12    
{txt}114{com}.         local rss_log_9_12=e(rss)
{txt}115{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' logtermPHI if age >=5 & age <=8    // includes predicted relative time/goods expenditure
{txt}116{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt}117{com}.         capture noisily test med_scoll med_collplus
{txt}118{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt}119{com}.         estimates store est_PHI_5_8
{txt}120{com}.         local rss_PHI_5_8=e(rss)
{txt}121{com}.         
.         regress ln_invratio_imp  ln_pratio_`pvar'  `Zall5' logtermPHI if age >=9 & age <=12    // includes predicted relative time/goods expenditure
{txt}122{com}.         nlcom gamma: _b[ln_pratio_`pvar']/(_b[ln_pratio_`pvar'] -1.0)
{txt}123{com}.         capture noisily test med_scoll med_collplus
{txt}124{com}.         capture noisily test fed_all_scoll fed_all_collplus
{txt}125{com}.         estimates store est_PHI_9_12
{txt}126{com}.         local rss_PHI_9_12=e(rss)
{txt}127{com}.         
.         
.         
.         **F-stat:  compare coefficients for all children vs children ages 5-8 and 9-12
. 
. local k2_ols5=13
{txt}128{com}. local k2_log=14
{txt}129{com}. local k2_PHI=14
{txt}130{com}. 
. local k_PHI_age=17
{txt}131{com}. local k2_PHI_age=3
{txt}132{com}. 
. 
. foreach var in "ols5" "log" "PHI" {c -(}
{txt}133{com}. 
. local F_`var'=(`n_`var''-2*`k2_`var'')/`k2_`var''*(`rss_`var''-(`rss_`var'_5_8'+`rss_`var'_9_12'))/(`rss_`var'_5_8'+`rss_`var'_9_12')
{txt}134{com}. local pf_`var'=Ftail(`k2_`var'',`n_`var''-2*`k2_`var'',`F_`var'')
{txt}135{com}. local p_`var': display %4.3f `pf_`var''
{txt}136{com}. estadd scalar p_val = `pf_`var'' : est_`var'
{txt}137{com}. 
. {c )-}
{txt}138{com}. 
.         
.         
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> *** Appendix Table G-16: OLS estimates for child care/goods relative demand by child age
. //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
> 
.         esttab est_ols5_5_8 est_ols5_9_12 est_log_5_8 est_log_9_12 est_PHI_5_8 est_PHI_9_12  using "$tables/All_YPvpg_`pvar'_OLS_age.tex", coeflab(ln_pratio_`pvar' "$\ln(\tilde{c -(}P{c )-}_{c -(}c,i{c )-})$" curr_married "Married" age "Child's age" med_hs "Mother HS grad"  med_scoll "Mother some coll."  med_collplus "Mother coll+"   fed_all_hs "Marr x Father HS grad"  fed_all_scoll "Marr x Father some coll."  fed_all_collplus "Marr x Father coll+" f_all_age "Marr x Father's age" m_age "Mother's age" m_white "Mother white" m_pc97 "Mother's cogn. score" num_child "Num of children" num_0_5 "Num children age 0-5" logterm "$\ln(1+R_m +Marr\cdot R_f)$" logtermPHI "$\ln(1+e^{c -(}\Phi_m{c )-}+Marr\cdot e^{c -(}\Phi_f{c )-})$")  stats(r2 rss N, fmt(3 3 0))  ti(Appendix Table: OLS estimates for child care/goods relative demand by child age (ALL, `pvar', 1-2 kids ages 0-12, 2002 and 2007)) cells(b(star fmt(3)) se(par fmt(3)))  varwidth(55) modelwidth(10) wrap style(tex) mtitles("Ages 5-8" "Ages 9-12" "Ages 5-8" "Ages 9-12" "Ages 5-8" "Ages 9-12") addnotes("p-value for (1) and (2) =  `p_ols5', p-value for (3) and (4) =  `p_log', p-value for (5) and (6) = `p_PHI'")  replace 
{txt}139{com}. 
.         drop d_ln_hhinv nonmiss_Yvg 
{txt}140{com}.         
. {c )-} // end loop over pvar



ALL HH, CHILD CARE VS. GOODS

Price measure: 4ca


{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       347
{txt}{hline 13}{c +}{hline 34}   F(12, 334)      = {res}     3.99
{txt}       Model {c |} {res} 65.1093226        12  5.42577688   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 453.779728       334  1.35862194   {txt}R-squared       ={res}    0.1255
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0941
{txt}       Total {c |} {res}  518.88905       346  1.49967934   {txt}Root MSE        =   {res} 1.1656

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .6498913{col 30}{space 2} .2042021{col 41}{space 1}    3.18{col 50}{space 3}0.002{col 58}{space 4} .2482069{col 71}{space 3} 1.051576
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2}  .834789{col 30}{space 2} .5729723{col 41}{space 1}    1.46{col 50}{space 3}0.146{col 58}{space 4}-.2923001{col 71}{space 3} 1.961878
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.1235225{col 30}{space 2} .0357705{col 41}{space 1}   -3.45{col 50}{space 3}0.001{col 58}{space 4}-.1938863{col 71}{space 3}-.0531587
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2}-.0545783{col 30}{space 2} .1614961{col 41}{space 1}   -0.34{col 50}{space 3}0.736{col 58}{space 4}-.3722561{col 71}{space 3} .2630994
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.3153154{col 30}{space 2} .1690761{col 41}{space 1}   -1.86{col 50}{space 3}0.063{col 58}{space 4}-.6479037{col 71}{space 3} .0172728
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0023736{col 30}{space 2} .0133993{col 41}{space 1}    0.18{col 50}{space 3}0.860{col 58}{space 4}-.0239839{col 71}{space 3} .0287312
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .1024446{col 30}{space 2} .2081371{col 41}{space 1}    0.49{col 50}{space 3}0.623{col 58}{space 4}-.3069802{col 71}{space 3} .5118693
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.4352918{col 30}{space 2} .2190415{col 41}{space 1}   -1.99{col 50}{space 3}0.048{col 58}{space 4}-.8661666{col 71}{space 3}-.0044171
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2} -.017108{col 30}{space 2} .0148714{col 41}{space 1}   -1.15{col 50}{space 3}0.251{col 58}{space 4}-.0463614{col 71}{space 3} .0121455
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.3281448{col 30}{space 2}  .139894{col 41}{space 1}   -2.35{col 50}{space 3}0.020{col 58}{space 4}-.6033293{col 71}{space 3}-.0529604
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2}-.0304874{col 30}{space 2} .1430166{col 41}{space 1}   -0.21{col 50}{space 3}0.831{col 58}{space 4}-.3118143{col 71}{space 3} .2508395
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .0954052{col 30}{space 2}  .111848{col 41}{space 1}    0.85{col 50}{space 3}0.394{col 58}{space 4}-.1246101{col 71}{space 3} .3154205
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .9330048{col 30}{space 2} .5907579{col 41}{space 1}    1.58{col 50}{space 3}0.115{col 58}{space 4}-.2290703{col 71}{space 3}  2.09508
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.856256{col 26}{space 2} 1.665922{col 37}{space 1}   -1.11{col 46}{space 3}0.265{col 54}{space 4}-5.121403{col 67}{space 3} 1.408891
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   334) ={res}    2.09
{txt}{col 13}Prob > F ={res}    0.1249

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   334) ={res}    2.25
{txt}{col 13}Prob > F ={res}    0.1069

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       347
{txt}{hline 13}{c +}{hline 34}   F(13, 333)      = {res}     3.68
{txt}       Model {c |} {res}  65.256424        13  5.01972493   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 453.632626       333  1.36226014   {txt}R-squared       ={res}    0.1258
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0916
{txt}       Total {c |} {res}  518.88905       346  1.49967934   {txt}Root MSE        =   {res} 1.1672

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .5981941{col 30}{space 2} .2579929{col 41}{space 1}    2.32{col 50}{space 3}0.021{col 58}{space 4} .0906927{col 71}{space 3} 1.105695
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .8350597{col 30}{space 2} .5737395{col 41}{space 1}    1.46{col 50}{space 3}0.146{col 58}{space 4} -.293551{col 71}{space 3}  1.96367
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.1211251{col 30}{space 2} .0365538{col 41}{space 1}   -3.31{col 50}{space 3}0.001{col 58}{space 4}-.1930306{col 71}{space 3}-.0492196
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2}-.0616177{col 30}{space 2} .1631249{col 41}{space 1}   -0.38{col 50}{space 3}0.706{col 58}{space 4}-.3825029{col 71}{space 3} .2592675
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.3147302{col 30}{space 2} .1693117{col 41}{space 1}   -1.86{col 50}{space 3}0.064{col 58}{space 4}-.6477855{col 71}{space 3} .0183251
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0021844{col 30}{space 2} .0134295{col 41}{space 1}    0.16{col 50}{space 3}0.871{col 58}{space 4} -.024233{col 71}{space 3} .0286019
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .1035973{col 30}{space 2} .2084451{col 41}{space 1}    0.50{col 50}{space 3}0.620{col 58}{space 4}-.3064378{col 71}{space 3} .5136325
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.4386562{col 30}{space 2} .2195734{col 41}{space 1}   -2.00{col 50}{space 3}0.047{col 58}{space 4} -.870582{col 71}{space 3}-.0067305
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0170554{col 30}{space 2} .0148922{col 41}{space 1}   -1.15{col 50}{space 3}0.253{col 58}{space 4}-.0463499{col 71}{space 3} .0122392
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.3289125{col 30}{space 2} .1401007{col 41}{space 1}   -2.35{col 50}{space 3}0.019{col 58}{space 4}-.6045064{col 71}{space 3}-.0533185
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2}-.0289096{col 30}{space 2} .1432885{col 41}{space 1}   -0.20{col 50}{space 3}0.840{col 58}{space 4}-.3107743{col 71}{space 3}  .252955
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .0943559{col 30}{space 2} .1120432{col 41}{space 1}    0.84{col 50}{space 3}0.400{col 58}{space 4}-.1260457{col 71}{space 3} .3147576
{txt}{space 14}SR {c |}{col 18}{res}{space 2} 1.759731{col 30}{space 2} 5.355102{col 41}{space 1}    0.33{col 50}{space 3}0.743{col 58}{space 4}-8.774363{col 71}{space 3} 12.29382
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .8751575{col 30}{space 2}  .617186{col 41}{space 1}    1.42{col 50}{space 3}0.157{col 58}{space 4}-.3389173{col 71}{space 3} 2.089232
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.488764{col 26}{space 2} 1.597994{col 37}{space 1}   -0.93{col 46}{space 3}0.352{col 54}{space 4}-4.620775{col 67}{space 3} 1.643247
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   333) ={res}    2.03
{txt}{col 13}Prob > F ={res}    0.1336

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   333) ={res}    2.27
{txt}{col 13}Prob > F ={res}    0.1046
{txt}(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made)
(0 real changes made)
(0 real changes made)
(0 real changes made)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}ln_hhinvest {c |}{res}        249    2.492497    .8901869   .2972515   4.757396
{txt}(155 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}ln_hhinvest {c |}{res}      1,357    2.513257    .9363272    .080982   5.033554
{txt}{space 2}d_ln_hhinv {c |}{res}      1,357    .0261979    1.181586   -3.04318   3.206652



OLS with nonlinear terms -- without staff ratio

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       249
{txt}{hline 13}{c +}{hline 34}   F(13, 235)      = {res}     7.61
{txt}       Model {c |} {res} 109.401274        13  8.41548261   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 260.004627       235  1.10640267   {txt}R-squared       ={res}    0.2962
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2572
{txt}       Total {c |} {res} 369.405901       248  1.48953992   {txt}Root MSE        =   {res} 1.0519

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2}  .561792{col 30}{space 2} .2151143{col 41}{space 1}    2.61{col 50}{space 3}0.010{col 58}{space 4} .1379932{col 71}{space 3} .9855909
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .0796661{col 30}{space 2} .6452462{col 41}{space 1}    0.12{col 50}{space 3}0.902{col 58}{space 4} -1.19154{col 71}{space 3} 1.350872
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0767914{col 30}{space 2} .0404182{col 41}{space 1}   -1.90{col 50}{space 3}0.059{col 58}{space 4}-.1564197{col 71}{space 3} .0028369
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .0988621{col 30}{space 2} .1754777{col 41}{space 1}    0.56{col 50}{space 3}0.574{col 58}{space 4}-.2468483{col 71}{space 3} .4445725
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.0587888{col 30}{space 2} .1809507{col 41}{space 1}   -0.32{col 50}{space 3}0.746{col 58}{space 4}-.4152815{col 71}{space 3} .2977039
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0015672{col 30}{space 2} .0148467{col 41}{space 1}   -0.11{col 50}{space 3}0.916{col 58}{space 4}-.0308169{col 71}{space 3} .0276825
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .0888974{col 30}{space 2} .2282622{col 41}{space 1}    0.39{col 50}{space 3}0.697{col 58}{space 4}-.3608043{col 71}{space 3}  .538599
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.7162167{col 30}{space 2} .2411813{col 41}{space 1}   -2.97{col 50}{space 3}0.003{col 58}{space 4} -1.19137{col 71}{space 3}-.2410631
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0043593{col 30}{space 2} .0165915{col 41}{space 1}   -0.26{col 50}{space 3}0.793{col 58}{space 4}-.0370465{col 71}{space 3} .0283278
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.3299769{col 30}{space 2} .1471418{col 41}{space 1}   -2.24{col 50}{space 3}0.026{col 58}{space 4}-.6198624{col 71}{space 3}-.0400915
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .1895988{col 30}{space 2} .1573889{col 41}{space 1}    1.20{col 50}{space 3}0.230{col 58}{space 4}-.1204747{col 71}{space 3} .4996722
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .1291071{col 30}{space 2}  .114935{col 41}{space 1}    1.12{col 50}{space 3}0.262{col 58}{space 4}-.0973274{col 71}{space 3} .3555416
{txt}{space 9}logterm {c |}{col 18}{res}{space 2} .4976874{col 30}{space 2} .0758885{col 41}{space 1}    6.56{col 50}{space 3}0.000{col 58}{space 4} .3481788{col 71}{space 3}  .647196
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.7527601{col 30}{space 2} .6901153{col 41}{space 1}   -1.09{col 50}{space 3}0.276{col 58}{space 4}-2.112363{col 71}{space 3}  .606843
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.282021{col 26}{space 2} 1.120234{col 37}{space 1}   -1.14{col 46}{space 3}0.252{col 54}{space 4} -3.47764{col 67}{space 3} .9135967
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logterm])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} 9.445149{col 26}{space 2} 32.23124{col 37}{space 1}    0.29{col 46}{space 3}0.769{col 54}{space 4}-53.72692{col 67}{space 3} 72.61722
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   235) ={res}    0.47
{txt}{col 13}Prob > F ={res}    0.6261

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   235) ={res}    5.55
{txt}{col 13}Prob > F ={res}    0.0044

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       249
{txt}{hline 13}{c +}{hline 34}   F(17, 231)      = {res}    11.54
{txt}       Model {c |} {res} 169.667911        17  9.98046538   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 199.737989       231  .864666621   {txt}R-squared       ={res}    0.4593
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4195
{txt}       Total {c |} {res} 369.405901       248  1.48953992   {txt}Root MSE        =   {res} .92987

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .6645372{col 30}{space 2} .1915344{col 41}{space 1}    3.47{col 50}{space 3}0.001{col 58}{space 4} .2871596{col 71}{space 3} 1.041915
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .9965426{col 30}{space 2} .6890283{col 41}{space 1}    1.45{col 50}{space 3}0.149{col 58}{space 4}-.3610407{col 71}{space 3} 2.354126
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0587901{col 30}{space 2}  .035877{col 41}{space 1}   -1.64{col 50}{space 3}0.103{col 58}{space 4}-.1294781{col 71}{space 3} .0118979
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .2010555{col 30}{space 2} .1577737{col 41}{space 1}    1.27{col 50}{space 3}0.204{col 58}{space 4}-.1098039{col 71}{space 3}  .511915
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2} .3920505{col 30}{space 2} .1730431{col 41}{space 1}    2.27{col 50}{space 3}0.024{col 58}{space 4}  .051106{col 71}{space 3}  .732995
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0118375{col 30}{space 2} .0132613{col 41}{space 1}    0.89{col 50}{space 3}0.373{col 58}{space 4} -.014291{col 71}{space 3} .0379661
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .0519245{col 30}{space 2} .2024444{col 41}{space 1}    0.26{col 50}{space 3}0.798{col 58}{space 4} -.346949{col 71}{space 3} .4507981
{txt}fed_all_collplus {c |}{col 18}{res}{space 2} -.344854{col 30}{space 2} .2180311{col 41}{space 1}   -1.58{col 50}{space 3}0.115{col 58}{space 4}-.7744379{col 71}{space 3} .0847298
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0147984{col 30}{space 2} .0147924{col 41}{space 1}   -1.00{col 50}{space 3}0.318{col 58}{space 4}-.0439438{col 71}{space 3} .0143469
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.0806405{col 30}{space 2} .1339285{col 41}{space 1}   -0.60{col 50}{space 3}0.548{col 58}{space 4}-.3445181{col 71}{space 3} .1832371
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .3020214{col 30}{space 2}  .140282{col 41}{space 1}    2.15{col 50}{space 3}0.032{col 58}{space 4} .0256256{col 71}{space 3} .5784171
{txt}{space 7}num_child {c |}{col 18}{res}{space 2}-.0393386{col 30}{space 2} .1041634{col 41}{space 1}   -0.38{col 50}{space 3}0.706{col 58}{space 4}-.2445704{col 71}{space 3} .1658933
{txt}{space 9}logterm {c |}{col 18}{res}{space 2}-.1247627{col 30}{space 2} .1583675{col 41}{space 1}   -0.79{col 50}{space 3}0.432{col 58}{space 4}-.4367921{col 71}{space 3} .1872666
{txt}{space 10}term_f {c |}{col 18}{res}{space 2} .2286882{col 30}{space 2} 4.502928{col 41}{space 1}    0.05{col 50}{space 3}0.960{col 58}{space 4}-8.643371{col 71}{space 3} 9.100747
{txt}{space 10}term_m {c |}{col 18}{res}{space 2}-3.354914{col 30}{space 2} 4.323856{col 41}{space 1}   -0.78{col 50}{space 3}0.439{col 58}{space 4}-11.87415{col 71}{space 3} 5.164322
{txt}{space 10}term_g {c |}{col 18}{res}{space 2}-2.851112{col 30}{space 2} 6.246017{col 41}{space 1}   -0.46{col 50}{space 3}0.648{col 58}{space 4}-15.15756{col 71}{space 3} 9.455332
{txt}{space 6}d_ln_hhinv {c |}{col 18}{res}{space 2}-.6881003{col 30}{space 2} .0836395{col 41}{space 1}   -8.23{col 50}{space 3}0.000{col 58}{space 4}-.8528941{col 71}{space 3}-.5233065
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .2455834{col 30}{space 2} .7959918{col 41}{space 1}    0.31{col 50}{space 3}0.758{col 58}{space 4}-1.322749{col 71}{space 3} 1.813915
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.980956{col 26}{space 2} 1.701993{col 37}{space 1}   -1.16{col 46}{space 3}0.244{col 54}{space 4}-5.316802{col 67}{space 3}  1.35489
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logterm])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-1.443938{col 26}{space 2} 1.118261{col 37}{space 1}   -1.29{col 46}{space 3}0.197{col 54}{space 4}-3.635689{col 67}{space 3} .7478133
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   231) ={res}    2.57
{txt}{col 13}Prob > F ={res}    0.0789

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   231) ={res}    1.51
{txt}{col 13}Prob > F ={res}    0.2232

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       310
{txt}{hline 13}{c +}{hline 34}   F(13, 296)      = {res}     3.39
{txt}       Model {c |} {res} 57.2525785        13   4.4040445   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 384.136117       296  1.29775715   {txt}R-squared       ={res}    0.1297
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0915
{txt}       Total {c |} {res} 441.388695       309  1.42844238   {txt}Root MSE        =   {res} 1.1392

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .5184031{col 30}{space 2} .2118829{col 41}{space 1}    2.45{col 50}{space 3}0.015{col 58}{space 4} .1014152{col 71}{space 3} .9353909
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .3544235{col 30}{space 2} .6206831{col 41}{space 1}    0.57{col 50}{space 3}0.568{col 58}{space 4}-.8670875{col 71}{space 3} 1.575934
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0823405{col 30}{space 2} .0514402{col 41}{space 1}   -1.60{col 50}{space 3}0.111{col 58}{space 4}-.1835754{col 71}{space 3} .0188943
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .0165726{col 30}{space 2}  .169188{col 41}{space 1}    0.10{col 50}{space 3}0.922{col 58}{space 4}-.3163912{col 71}{space 3} .3495363
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.2160657{col 30}{space 2} .1788148{col 41}{space 1}   -1.21{col 50}{space 3}0.228{col 58}{space 4}-.5679751{col 71}{space 3} .1358437
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0016902{col 30}{space 2} .0137749{col 41}{space 1}    0.12{col 50}{space 3}0.902{col 58}{space 4}-.0254188{col 71}{space 3} .0287993
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .1962993{col 30}{space 2} .2138658{col 41}{space 1}    0.92{col 50}{space 3}0.359{col 58}{space 4}-.2245908{col 71}{space 3} .6171894
{txt}fed_all_collplus {c |}{col 18}{res}{space 2} -.616115{col 30}{space 2} .2436522{col 41}{space 1}   -2.53{col 50}{space 3}0.012{col 58}{space 4}-1.095625{col 71}{space 3}-.1366049
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0110432{col 30}{space 2} .0154151{col 41}{space 1}   -0.72{col 50}{space 3}0.474{col 58}{space 4}-.0413803{col 71}{space 3} .0192939
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.2533954{col 30}{space 2} .1471529{col 41}{space 1}   -1.72{col 50}{space 3}0.086{col 58}{space 4}-.5429939{col 71}{space 3} .0362032
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .0144224{col 30}{space 2} .1564294{col 41}{space 1}    0.09{col 50}{space 3}0.927{col 58}{space 4}-.2934324{col 71}{space 3} .3222772
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .1044197{col 30}{space 2} .1165242{col 41}{space 1}    0.90{col 50}{space 3}0.371{col 58}{space 4}-.1249012{col 71}{space 3} .3337406
{txt}{space 6}logtermPHI {c |}{col 18}{res}{space 2} .3935473{col 30}{space 2} .2959121{col 41}{space 1}    1.33{col 50}{space 3}0.185{col 58}{space 4}-.1888108{col 71}{space 3} .9759055
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.3039056{col 30}{space 2} 1.146047{col 41}{space 1}   -0.27{col 50}{space 3}0.791{col 58}{space 4}-2.559338{col 71}{space 3} 1.951526
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.076425{col 26}{space 2}  .913542{col 37}{space 1}   -1.18{col 46}{space 3}0.239{col 54}{space 4}-2.866935{col 67}{space 3} .7140843
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logtermPHI])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-5.887627{col 26}{space 2} 24.35209{col 37}{space 1}   -0.24{col 46}{space 3}0.809{col 54}{space 4}-53.61685{col 67}{space 3}  41.8416
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   296) ={res}    1.18
{txt}{col 13}Prob > F ={res}    0.3073

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   296) ={res}    3.32
{txt}{col 13}Prob > F ={res}    0.0375



OLS with nonlinear terms -- with staff ratio
{txt}(0 real changes made)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}ln_hhinvest {c |}{res}        249    2.492497    .8901869   .2972515   4.757396
{txt}(0 real changes made)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}ln_hhinvest {c |}{res}      1,357    2.513257    .9363272    .080982   5.033554
{txt}{space 2}d_ln_hhinv {c |}{res}      1,357    .0261979    1.181586   -3.04318   3.206652

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       249
{txt}{hline 13}{c +}{hline 34}   F(14, 234)      = {res}     7.07
{txt}       Model {c |} {res} 109.782358        14  7.84159698   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 259.623543       234  1.10950232   {txt}R-squared       ={res}    0.2972
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2551
{txt}       Total {c |} {res} 369.405901       248  1.48953992   {txt}Root MSE        =   {res} 1.0533

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .6588281{col 30}{space 2} .2716945{col 41}{space 1}    2.42{col 50}{space 3}0.016{col 58}{space 4} .1235481{col 71}{space 3} 1.194108
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .0876879{col 30}{space 2} .6462944{col 41}{space 1}    0.14{col 50}{space 3}0.892{col 58}{space 4}-1.185611{col 71}{space 3} 1.360987
{txt}{space 13}age {c |}{col 18}{res}{space 2} -.080687{col 30}{space 2}  .041017{col 41}{space 1}   -1.97{col 50}{space 3}0.050{col 58}{space 4}-.1614967{col 71}{space 3} .0001227
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .1087555{col 30}{space 2} .1765323{col 41}{space 1}    0.62{col 50}{space 3}0.538{col 58}{space 4}-.2390403{col 71}{space 3} .4565513
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.0628065{col 30}{space 2} .1813336{col 41}{space 1}   -0.35{col 50}{space 3}0.729{col 58}{space 4}-.4200615{col 71}{space 3} .2944486
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0007156{col 30}{space 2} .0149384{col 41}{space 1}   -0.05{col 50}{space 3}0.962{col 58}{space 4}-.0301464{col 71}{space 3} .0287153
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .0809647{col 30}{space 2} .2289821{col 41}{space 1}    0.35{col 50}{space 3}0.724{col 58}{space 4}-.3701653{col 71}{space 3} .5320946
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.7034029{col 30}{space 2} .2425065{col 41}{space 1}   -2.90{col 50}{space 3}0.004{col 58}{space 4}-1.181178{col 71}{space 3}-.2256278
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0047423{col 30}{space 2} .0166276{col 41}{space 1}   -0.29{col 50}{space 3}0.776{col 58}{space 4}-.0375012{col 71}{space 3} .0280166
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.3245957{col 30}{space 2} .1476335{col 41}{space 1}   -2.20{col 50}{space 3}0.029{col 58}{space 4}-.6154565{col 71}{space 3} -.033735
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .1979955{col 30}{space 2} .1582591{col 41}{space 1}    1.25{col 50}{space 3}0.212{col 58}{space 4}-.1137992{col 71}{space 3} .5097901
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .1266647{col 30}{space 2} .1151713{col 41}{space 1}    1.10{col 50}{space 3}0.273{col 58}{space 4}-.1002404{col 71}{space 3} .3535698
{txt}{space 14}SR {c |}{col 18}{res}{space 2}-3.444907{col 30}{space 2} 5.878024{col 41}{space 1}   -0.59{col 50}{space 3}0.558{col 58}{space 4}-15.02552{col 71}{space 3} 8.135703
{txt}{space 9}logterm {c |}{col 18}{res}{space 2} .4994329{col 30}{space 2}  .076053{col 41}{space 1}    6.57{col 50}{space 3}0.000{col 58}{space 4} .3495967{col 71}{space 3}  .649269
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.6571228{col 30}{space 2} .7100865{col 41}{space 1}   -0.93{col 50}{space 3}0.356{col 58}{space 4}-2.056102{col 71}{space 3} .7418567
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.931074{col 26}{space 2}  2.33418{col 37}{space 1}   -0.83{col 46}{space 3}0.408{col 54}{space 4}-6.505983{col 67}{space 3} 2.643836
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logterm])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2} 4.162922{col 26}{space 2} 5.752165{col 37}{space 1}    0.72{col 46}{space 3}0.469{col 54}{space 4}-7.111114{col 67}{space 3} 15.43696
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   234) ={res}    0.54
{txt}{col 13}Prob > F ={res}    0.5815

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   234) ={res}    5.36
{txt}{col 13}Prob > F ={res}    0.0053

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       249
{txt}{hline 13}{c +}{hline 34}   F(18, 230)      = {res}    10.90
{txt}       Model {c |} {res} 170.053989        18  9.44744384   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 199.351912       230  .866747443   {txt}R-squared       ={res}    0.4603
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4181
{txt}       Total {c |} {res} 369.405901       248  1.48953992   {txt}Root MSE        =   {res} .93099

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .7640483{col 30}{space 2} .2429091{col 41}{space 1}    3.15{col 50}{space 3}0.002{col 58}{space 4} .2854368{col 71}{space 3}  1.24266
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2}  .997593{col 30}{space 2} .6898587{col 41}{space 1}    1.45{col 50}{space 3}0.150{col 58}{space 4}-.3616575{col 71}{space 3} 2.356844
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0626322{col 30}{space 2} .0363786{col 41}{space 1}   -1.72{col 50}{space 3}0.086{col 58}{space 4}  -.13431{col 71}{space 3} .0090456
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .2127377{col 30}{space 2} .1589303{col 41}{space 1}    1.34{col 50}{space 3}0.182{col 58}{space 4}-.1004076{col 71}{space 3} .5258831
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2} .3897427{col 30}{space 2} .1732857{col 41}{space 1}    2.25{col 50}{space 3}0.025{col 58}{space 4} .0483124{col 71}{space 3}  .731173
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0126506{col 30}{space 2}  .013333{col 41}{space 1}    0.95{col 50}{space 3}0.344{col 58}{space 4}-.0136199{col 71}{space 3}  .038921
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .0430053{col 30}{space 2}  .203128{col 41}{space 1}    0.21{col 50}{space 3}0.833{col 58}{space 4}-.3572242{col 71}{space 3} .4432347
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.3318025{col 30}{space 2} .2191675{col 41}{space 1}   -1.51{col 50}{space 3}0.131{col 58}{space 4}-.7636352{col 71}{space 3} .1000302
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0150778{col 30}{space 2} .0148161{col 41}{space 1}   -1.02{col 50}{space 3}0.310{col 58}{space 4}-.0442705{col 71}{space 3} .0141149
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.0752395{col 30}{space 2} .1343336{col 41}{space 1}   -0.56{col 50}{space 3}0.576{col 58}{space 4}-.3399212{col 71}{space 3} .1894422
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .3109145{col 30}{space 2} .1410813{col 41}{space 1}    2.20{col 50}{space 3}0.029{col 58}{space 4} .0329374{col 71}{space 3} .5888915
{txt}{space 7}num_child {c |}{col 18}{res}{space 2}-.0425327{col 30}{space 2} .1043985{col 41}{space 1}   -0.41{col 50}{space 3}0.684{col 58}{space 4}-.2482323{col 71}{space 3} .1631669
{txt}{space 14}SR {c |}{col 18}{res}{space 2}-3.504045{col 30}{space 2} 5.250234{col 41}{space 1}   -0.67{col 50}{space 3}0.505{col 58}{space 4}-13.84875{col 71}{space 3} 6.840657
{txt}{space 9}logterm {c |}{col 18}{res}{space 2}-.1115943{col 30}{space 2} .1597808{col 41}{space 1}   -0.70{col 50}{space 3}0.486{col 58}{space 4}-.4264155{col 71}{space 3}  .203227
{txt}{space 10}term_f {c |}{col 18}{res}{space 2}-.1522701{col 30}{space 2} 4.544334{col 41}{space 1}   -0.03{col 50}{space 3}0.973{col 58}{space 4}-9.106116{col 71}{space 3} 8.801576
{txt}{space 10}term_m {c |}{col 18}{res}{space 2}-2.970675{col 30}{space 2}  4.36717{col 41}{space 1}   -0.68{col 50}{space 3}0.497{col 58}{space 4}-11.57545{col 71}{space 3} 5.634099
{txt}{space 10}term_g {c |}{col 18}{res}{space 2}-2.366974{col 30}{space 2}  6.29546{col 41}{space 1}   -0.38{col 50}{space 3}0.707{col 58}{space 4}-14.77112{col 71}{space 3} 10.03717
{txt}{space 6}d_ln_hhinv {c |}{col 18}{res}{space 2} -.688331{col 30}{space 2} .0837408{col 41}{space 1}   -8.22{col 50}{space 3}0.000{col 58}{space 4}-.8533282{col 71}{space 3}-.5233339
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .2960192{col 30}{space 2} .8005239{col 41}{space 1}    0.37{col 50}{space 3}0.712{col 58}{space 4}-1.281278{col 71}{space 3} 1.873317
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-3.238156{col 26}{space 2} 4.363124{col 37}{space 1}   -0.74{col 46}{space 3}0.458{col 54}{space 4}-11.78972{col 67}{space 3}  5.31341
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logterm])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-2.198409{col 26}{space 2} 2.500625{col 37}{space 1}   -0.88{col 46}{space 3}0.379{col 54}{space 4}-7.099544{col 67}{space 3} 2.702725
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   230) ={res}    2.53
{txt}{col 13}Prob > F ={res}    0.0818

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   230) ={res}    1.42
{txt}{col 13}Prob > F ={res}    0.2449

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       310
{txt}{hline 13}{c +}{hline 34}   F(14, 295)      = {res}     3.14
{txt}       Model {c |} {res} 57.2574026        14  4.08981447   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 384.131293       295  1.30213998   {txt}R-squared       ={res}    0.1297
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0884
{txt}       Total {c |} {res} 441.388695       309  1.42844238   {txt}Root MSE        =   {res} 1.1411

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .5087073{col 30}{space 2} .2653699{col 41}{space 1}    1.92{col 50}{space 3}0.056{col 58}{space 4}-.0135508{col 71}{space 3} 1.030965
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .3551888{col 30}{space 2} .6218574{col 41}{space 1}    0.57{col 50}{space 3}0.568{col 58}{space 4}-.8686504{col 71}{space 3} 1.579028
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0821886{col 30}{space 2} .0515874{col 41}{space 1}   -1.59{col 50}{space 3}0.112{col 58}{space 4}-.1837146{col 71}{space 3} .0193374
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2}  .015626{col 30}{space 2} .1701855{col 41}{space 1}    0.09{col 50}{space 3}0.927{col 58}{space 4}-.3193055{col 71}{space 3} .3505575
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.2156674{col 30}{space 2}  .179236{col 41}{space 1}   -1.20{col 50}{space 3}0.230{col 58}{space 4}-.5684107{col 71}{space 3} .1370759
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0016411{col 30}{space 2} .0138217{col 41}{space 1}    0.12{col 50}{space 3}0.906{col 58}{space 4}-.0255606{col 71}{space 3} .0288427
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .1962449{col 30}{space 2} .2142284{col 41}{space 1}    0.92{col 50}{space 3}0.360{col 58}{space 4}-.2253649{col 71}{space 3} .6178546
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.6166153{col 30}{space 2} .2442017{col 41}{space 1}   -2.53{col 50}{space 3}0.012{col 58}{space 4}-1.097213{col 71}{space 3}-.1360171
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0110217{col 30}{space 2} .0154452{col 41}{space 1}   -0.71{col 50}{space 3}0.476{col 58}{space 4}-.0414184{col 71}{space 3}  .019375
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.2539242{col 30}{space 2}  .147657{col 41}{space 1}   -1.72{col 50}{space 3}0.087{col 58}{space 4}-.5445188{col 71}{space 3} .0366705
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .0147466{col 30}{space 2} .1567839{col 41}{space 1}    0.09{col 50}{space 3}0.925{col 58}{space 4}-.2938101{col 71}{space 3} .3233032
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .1044827{col 30}{space 2} .1167254{col 41}{space 1}    0.90{col 50}{space 3}0.371{col 58}{space 4}-.1252373{col 71}{space 3} .3342028
{txt}{space 14}SR {c |}{col 18}{res}{space 2} .3402232{col 30}{space 2} 5.589671{col 41}{space 1}    0.06{col 50}{space 3}0.952{col 58}{space 4}-10.66046{col 71}{space 3} 11.34091
{txt}{space 6}logtermPHI {c |}{col 18}{res}{space 2} .3915274{col 30}{space 2} .2982634{col 41}{space 1}    1.31{col 50}{space 3}0.190{col 58}{space 4}-.1954664{col 71}{space 3} .9785211
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.3077829{col 30}{space 2} 1.149746{col 41}{space 1}   -0.27{col 50}{space 3}0.789{col 58}{space 4}-2.570527{col 71}{space 3} 1.954962
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-1.035447{col 26}{space 2} 1.099439{col 37}{space 1}   -0.94{col 46}{space 3}0.346{col 54}{space 4}-3.190307{col 67}{space 3} 1.119414
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

         rho:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0 + _b[logtermPHI])

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rho {c |}{col 14}{res}{space 2}-5.099039{col 26}{space 2} 22.01676{col 37}{space 1}   -0.23{col 46}{space 3}0.817{col 54}{space 4} -48.2511{col 67}{space 3} 38.05302
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   295) ={res}    1.16
{txt}{col 13}Prob > F ={res}    0.3164

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   295) ={res}    3.31
{txt}{col 13}Prob > F ={res}    0.0379
{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/All_YPvpg_4ca_OLS.tex"'})



HECKMAN TWO-STEP Estimates


{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}       848
{txt}(regression model with sample selection)              Selected    = {res}       204
                                                {txt}      Nonselected = {res}       644

                                                {txt}Wald chi2({res}12{txt})     =  {res}    30.81
                                                {txt}Prob > chi2       =     {res}0.0021

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_invratio_imp  {txt}{c |}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .6566238{col 30}{space 2} .2808294{col 41}{space 1}    2.34{col 50}{space 3}0.019{col 58}{space 4} .1062083{col 71}{space 3} 1.207039
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .1943514{col 30}{space 2} .7961466{col 41}{space 1}    0.24{col 50}{space 3}0.807{col 58}{space 4}-1.366067{col 71}{space 3}  1.75477
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.2085535{col 30}{space 2} .0817605{col 41}{space 1}   -2.55{col 50}{space 3}0.011{col 58}{space 4}-.3688012{col 71}{space 3}-.0483059
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .3217038{col 30}{space 2} .2801816{col 41}{space 1}    1.15{col 50}{space 3}0.251{col 58}{space 4}-.2274419{col 71}{space 3} .8708496
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2} .0313527{col 30}{space 2} .3596882{col 41}{space 1}    0.09{col 50}{space 3}0.931{col 58}{space 4}-.6736231{col 71}{space 3} .7363286
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0303964{col 30}{space 2} .0239175{col 41}{space 1}   -1.27{col 50}{space 3}0.204{col 58}{space 4}-.0772738{col 71}{space 3}  .016481
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .2213005{col 30}{space 2} .3108723{col 41}{space 1}    0.71{col 50}{space 3}0.477{col 58}{space 4}-.3879981{col 71}{space 3} .8305991
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.4169296{col 30}{space 2} .3220825{col 41}{space 1}   -1.29{col 50}{space 3}0.195{col 58}{space 4}  -1.0482{col 71}{space 3} .2143404
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2} .0006016{col 30}{space 2}  .021879{col 41}{space 1}    0.03{col 50}{space 3}0.978{col 58}{space 4}-.0422805{col 71}{space 3} .0434836
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.3180064{col 30}{space 2} .2099175{col 41}{space 1}   -1.51{col 50}{space 3}0.130{col 58}{space 4}-.7294373{col 71}{space 3} .0934244
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .2328023{col 30}{space 2} .2195101{col 41}{space 1}    1.06{col 50}{space 3}0.289{col 58}{space 4}-.1974295{col 71}{space 3}  .663034
{txt}{space 7}num_child {c |}{col 18}{res}{space 2}-.1937217{col 30}{space 2} .2168467{col 41}{space 1}   -0.89{col 50}{space 3}0.372{col 58}{space 4}-.6187335{col 71}{space 3} .2312901
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 1.799573{col 30}{space 2} .9026232{col 41}{space 1}    1.99{col 50}{space 3}0.046{col 58}{space 4} .0304636{col 71}{space 3} 3.568682
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_chcare_imp   {txt}{c |}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .0478482{col 30}{space 2} .1712282{col 41}{space 1}    0.28{col 50}{space 3}0.780{col 58}{space 4}-.2877529{col 71}{space 3} .3834494
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2}-.0150736{col 30}{space 2} .4904268{col 41}{space 1}   -0.03{col 50}{space 3}0.975{col 58}{space 4}-.9762924{col 71}{space 3} .9461453
{txt}{space 13}age {c |}{col 18}{res}{space 2}  -.12936{col 30}{space 2} .0309387{col 41}{space 1}   -4.18{col 50}{space 3}0.000{col 58}{space 4}-.1899986{col 71}{space 3}-.0687213
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2}  .338889{col 30}{space 2} .1336318{col 41}{space 1}    2.54{col 50}{space 3}0.011{col 58}{space 4} .0769754{col 71}{space 3} .6008025
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2} .6397836{col 30}{space 2} .1488677{col 41}{space 1}    4.30{col 50}{space 3}0.000{col 58}{space 4} .3480082{col 71}{space 3}  .931559
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0284286{col 30}{space 2}  .012918{col 41}{space 1}   -2.20{col 50}{space 3}0.028{col 58}{space 4}-.0537474{col 71}{space 3}-.0031098
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2}-.1860154{col 30}{space 2} .1762691{col 41}{space 1}   -1.06{col 50}{space 3}0.291{col 58}{space 4}-.5314965{col 71}{space 3} .1594658
{txt}fed_all_collplus {c |}{col 18}{res}{space 2} .1825997{col 30}{space 2} .1815093{col 41}{space 1}    1.01{col 50}{space 3}0.314{col 58}{space 4}-.1731521{col 71}{space 3} .5383514
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0010921{col 30}{space 2} .0128567{col 41}{space 1}   -0.08{col 50}{space 3}0.932{col 58}{space 4}-.0262907{col 71}{space 3} .0241065
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.0030019{col 30}{space 2} .1286497{col 41}{space 1}   -0.02{col 50}{space 3}0.981{col 58}{space 4}-.2551507{col 71}{space 3} .2491468
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .0939662{col 30}{space 2} .1355224{col 41}{space 1}    0.69{col 50}{space 3}0.488{col 58}{space 4}-.1716529{col 71}{space 3} .3595853
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} -.250398{col 30}{space 2} .1237682{col 41}{space 1}   -2.02{col 50}{space 3}0.043{col 58}{space 4}-.4929791{col 71}{space 3}-.0078168
{txt}{space 16} {c |}
{space 12}year {c |}
{space 11}2007  {c |}{col 18}{res}{space 2}-.6374484{col 30}{space 2} .1717918{col 41}{space 1}   -3.71{col 50}{space 3}0.000{col 58}{space 4}-.9741542{col 71}{space 3}-.3007426
{txt}{space 16} {c |}
{space 2}ind_same_state {c |}{col 18}{res}{space 2}-.0118177{col 30}{space 2} .1214323{col 41}{space 1}   -0.10{col 50}{space 3}0.922{col 58}{space 4}-.2498206{col 71}{space 3} .2261851
{txt}{space 6}live_grand {c |}{col 18}{res}{space 2}-.7432194{col 30}{space 2} .3147315{col 41}{space 1}   -2.36{col 50}{space 3}0.018{col 58}{space 4}-1.360082{col 71}{space 3}-.1263571
{txt}{space 7}old_child {c |}{col 18}{res}{space 2} -.061062{col 30}{space 2} .1618957{col 41}{space 1}   -0.38{col 50}{space 3}0.706{col 58}{space 4}-.3783718{col 71}{space 3} .2562477
{txt}{space 1}old_child_2plus {c |}{col 18}{res}{space 2}-.4570036{col 30}{space 2} .3688726{col 41}{space 1}   -1.24{col 50}{space 3}0.215{col 58}{space 4}-1.179981{col 71}{space 3} .2659735
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 1.862565{col 30}{space 2} .5859564{col 41}{space 1}    3.18{col 50}{space 3}0.001{col 58}{space 4} .7141112{col 71}{space 3} 3.011018
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills           {txt}{c |}
{space 10}lambda {c |}{col 18}{res}{space 2} .9232667{col 30}{space 2} .5395568{col 41}{space 1}    1.71{col 50}{space 3}0.087{col 58}{space 4}-.1342452{col 71}{space 3} 1.980778
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             rho {c |} {res}   0.66153
           {txt}sigma {c |} {res} 1.3956465
{txt}{hline 17}{c BT}{hline 64}
{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}     1,318
{txt}(regression model with sample selection)              Selected    = {res}       338
                                                {txt}      Nonselected = {res}       980

                                                {txt}Wald chi2({res}12{txt})     =  {res}    44.49
                                                {txt}Prob > chi2       =     {res}0.0000

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_invratio_imp   {txt}{c |}
{space 4}ln_pratio_4ca {c |}{col 19}{res}{space 2} .6547398{col 31}{space 2} .2186861{col 42}{space 1}    2.99{col 51}{space 3}0.003{col 59}{space 4}  .226123{col 72}{space 3} 1.083357
{txt}{space 5}curr_married {c |}{col 19}{res}{space 2} .8252704{col 31}{space 2} .6263114{col 42}{space 1}    1.32{col 51}{space 3}0.188{col 59}{space 4}-.4022775{col 72}{space 3} 2.052818
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.2419355{col 31}{space 2} .0632281{col 42}{space 1}   -3.83{col 51}{space 3}0.000{col 59}{space 4}-.3658603{col 72}{space 3}-.1180106
{txt}{space 8}med_scoll {c |}{col 19}{res}{space 2} .2532315{col 31}{space 2} .2027357{col 42}{space 1}    1.25{col 51}{space 3}0.212{col 59}{space 4}-.1441232{col 72}{space 3} .6505862
{txt}{space 5}med_collplus {c |}{col 19}{res}{space 2}   .05388{col 31}{space 2} .2338849{col 42}{space 1}    0.23{col 51}{space 3}0.818{col 59}{space 4} -.404526{col 72}{space 3}  .512286
{txt}{space 12}m_age {c |}{col 19}{res}{space 2}-.0043101{col 31}{space 2}  .014432{col 42}{space 1}   -0.30{col 51}{space 3}0.765{col 59}{space 4}-.0325962{col 72}{space 3}  .023976
{txt}{space 4}fed_all_scoll {c |}{col 19}{res}{space 2} .0711652{col 31}{space 2} .2231475{col 42}{space 1}    0.32{col 51}{space 3}0.750{col 59}{space 4} -.366196{col 72}{space 3} .5085263
{txt}{space 1}fed_all_collplus {c |}{col 19}{res}{space 2}-.4546249{col 31}{space 2} .2357435{col 42}{space 1}   -1.93{col 51}{space 3}0.054{col 59}{space 4}-.9166738{col 72}{space 3} .0074239
{txt}{space 8}f_all_age {c |}{col 19}{res}{space 2}-.0177172{col 31}{space 2} .0160935{col 42}{space 1}   -1.10{col 51}{space 3}0.271{col 59}{space 4}-.0492599{col 72}{space 3} .0138256
{txt}{space 10}m_white {c |}{col 19}{res}{space 2}-.3420207{col 31}{space 2} .1519123{col 42}{space 1}   -2.25{col 51}{space 3}0.024{col 59}{space 4}-.6397634{col 72}{space 3} -.044278
{txt}{space 10}num_0_5 {c |}{col 19}{res}{space 2} .1754002{col 31}{space 2} .1773478{col 42}{space 1}    0.99{col 51}{space 3}0.323{col 59}{space 4} -.172195{col 72}{space 3} .5229954
{txt}{space 8}num_child {c |}{col 19}{res}{space 2}-.0775434{col 31}{space 2} .1440184{col 42}{space 1}   -0.54{col 51}{space 3}0.590{col 59}{space 4}-.3598143{col 72}{space 3} .2047274
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.228099{col 31}{space 2} .6428765{col 42}{space 1}    1.91{col 51}{space 3}0.056{col 59}{space 4}-.0319161{col 72}{space 3} 2.488113
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_chcare_imp    {txt}{c |}
{space 4}ln_pratio_4ca {c |}{col 19}{res}{space 2}  .103191{col 31}{space 2} .1348416{col 42}{space 1}    0.77{col 51}{space 3}0.444{col 59}{space 4}-.1610937{col 72}{space 3} .3674757
{txt}{space 5}curr_married {c |}{col 19}{res}{space 2}  .144169{col 31}{space 2} .3794538{col 42}{space 1}    0.38{col 51}{space 3}0.704{col 59}{space 4}-.5995468{col 72}{space 3} .8878849
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.1348854{col 31}{space 2} .0233724{col 42}{space 1}   -5.77{col 51}{space 3}0.000{col 59}{space 4}-.1806944{col 72}{space 3}-.0890764
{txt}{space 8}med_scoll {c |}{col 19}{res}{space 2}  .338533{col 31}{space 2} .1008443{col 42}{space 1}    3.36{col 51}{space 3}0.001{col 59}{space 4} .1408818{col 72}{space 3} .5361841
{txt}{space 5}med_collplus {c |}{col 19}{res}{space 2} .5014151{col 31}{space 2} .1135894{col 42}{space 1}    4.41{col 51}{space 3}0.000{col 59}{space 4}  .278784{col 72}{space 3} .7240462
{txt}{space 12}m_age {c |}{col 19}{res}{space 2}-.0088288{col 31}{space 2} .0092034{col 42}{space 1}   -0.96{col 51}{space 3}0.337{col 59}{space 4}-.0268671{col 72}{space 3} .0092096
{txt}{space 4}fed_all_scoll {c |}{col 19}{res}{space 2}-.0595044{col 31}{space 2} .1354533{col 42}{space 1}   -0.44{col 51}{space 3}0.660{col 59}{space 4} -.324988{col 72}{space 3} .2059792
{txt}{space 1}fed_all_collplus {c |}{col 19}{res}{space 2}-.0085802{col 31}{space 2} .1460317{col 42}{space 1}   -0.06{col 51}{space 3}0.953{col 59}{space 4}-.2947971{col 72}{space 3} .2776367
{txt}{space 8}f_all_age {c |}{col 19}{res}{space 2}-.0056164{col 31}{space 2} .0094622{col 42}{space 1}   -0.59{col 51}{space 3}0.553{col 59}{space 4}-.0241619{col 72}{space 3} .0129291
{txt}{space 10}m_white {c |}{col 19}{res}{space 2}-.0822431{col 31}{space 2} .0946629{col 42}{space 1}   -0.87{col 51}{space 3}0.385{col 59}{space 4}-.2677789{col 72}{space 3} .1032928
{txt}{space 10}num_0_5 {c |}{col 19}{res}{space 2} .2976523{col 31}{space 2} .1057022{col 42}{space 1}    2.82{col 51}{space 3}0.005{col 59}{space 4} .0904797{col 72}{space 3} .5048248
{txt}{space 8}num_child {c |}{col 19}{res}{space 2}-.2427948{col 31}{space 2} .0939502{col 42}{space 1}   -2.58{col 51}{space 3}0.010{col 59}{space 4}-.4269338{col 72}{space 3}-.0586558
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2007  {c |}{col 19}{res}{space 2}-.7573664{col 31}{space 2} .1420622{col 42}{space 1}   -5.33{col 51}{space 3}0.000{col 59}{space 4}-1.035803{col 72}{space 3}-.4789297
{txt}{space 17} {c |}
{space 3}ind_same_state {c |}{col 19}{res}{space 2}-.0417147{col 31}{space 2} .0924264{col 42}{space 1}   -0.45{col 51}{space 3}0.652{col 59}{space 4}-.2228671{col 72}{space 3} .1394378
{txt}older_relative_hh {c |}{col 19}{res}{space 2}-.5396103{col 31}{space 2} .2247648{col 42}{space 1}   -2.40{col 51}{space 3}0.016{col 59}{space 4}-.9801412{col 72}{space 3}-.0990794
{txt}{space 8}old_child {c |}{col 19}{res}{space 2}-.1187269{col 31}{space 2} .1244992{col 42}{space 1}   -0.95{col 51}{space 3}0.340{col 59}{space 4}-.3627408{col 72}{space 3}  .125287
{txt}{space 2}old_child_2plus {c |}{col 19}{res}{space 2} .1411265{col 31}{space 2} .2167482{col 42}{space 1}    0.65{col 51}{space 3}0.515{col 59}{space 4}-.2836921{col 72}{space 3} .5659451
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.285701{col 31}{space 2} .4521778{col 42}{space 1}    2.84{col 51}{space 3}0.004{col 59}{space 4} .3994484{col 72}{space 3} 2.171953
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills            {txt}{c |}
{space 11}lambda {c |}{col 19}{res}{space 2} .9484996{col 31}{space 2} .4213791{col 42}{space 1}    2.25{col 51}{space 3}0.024{col 59}{space 4} .1226118{col 72}{space 3} 1.774387
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              rho {c |} {res}   0.68951
            {txt}sigma {c |} {res} 1.3756098
{txt}{hline 18}{c BT}{hline 64}
{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}     1,318
{txt}(regression model with sample selection)              Selected    = {res}       338
                                                {txt}      Nonselected = {res}       980

                                                {txt}Wald chi2({res}12{txt})     =  {res}    43.10
                                                {txt}Prob > chi2       =     {res}0.0000

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_invratio_imp  {txt}{c |}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .6534017{col 30}{space 2} .2165547{col 41}{space 1}    3.02{col 50}{space 3}0.003{col 58}{space 4} .2289624{col 71}{space 3} 1.077841
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2}  .832856{col 30}{space 2} .6218767{col 41}{space 1}    1.34{col 50}{space 3}0.180{col 58}{space 4}    -.386{col 71}{space 3} 2.051712
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.2331262{col 30}{space 2} .0666282{col 41}{space 1}   -3.50{col 50}{space 3}0.000{col 58}{space 4}-.3637152{col 71}{space 3}-.1025372
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .2315904{col 30}{space 2} .2062369{col 41}{space 1}    1.12{col 50}{space 3}0.261{col 58}{space 4}-.1726266{col 71}{space 3} .6358073
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}  .027637{col 30}{space 2} .2411622{col 41}{space 1}    0.11{col 50}{space 3}0.909{col 58}{space 4}-.4450322{col 71}{space 3} .5003061
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0040365{col 30}{space 2} .0143334{col 41}{space 1}   -0.28{col 50}{space 3}0.778{col 58}{space 4}-.0321294{col 71}{space 3} .0240564
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .0774173{col 30}{space 2} .2209728{col 41}{space 1}    0.35{col 50}{space 3}0.726{col 58}{space 4}-.3556814{col 71}{space 3}  .510516
{txt}fed_all_collplus {c |}{col 18}{res}{space 2} -.452826{col 30}{space 2} .2333344{col 41}{space 1}   -1.94{col 50}{space 3}0.052{col 58}{space 4}-.9101529{col 71}{space 3} .0045009
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0176032{col 30}{space 2} .0159917{col 41}{space 1}   -1.10{col 50}{space 3}0.271{col 58}{space 4}-.0489462{col 71}{space 3} .0137399
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.3446922{col 30}{space 2} .1505066{col 41}{space 1}   -2.29{col 50}{space 3}0.022{col 58}{space 4}-.6396797{col 71}{space 3}-.0497048
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .1582325{col 30}{space 2} .1793395{col 41}{space 1}    0.88{col 50}{space 3}0.378{col 58}{space 4}-.1932665{col 71}{space 3} .5097315
{txt}{space 7}num_child {c |}{col 18}{res}{space 2}-.0632644{col 30}{space 2}  .148165{col 41}{space 1}   -0.43{col 50}{space 3}0.669{col 58}{space 4}-.3536624{col 71}{space 3} .2271337
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 1.205668{col 30}{space 2}  .637589{col 41}{space 1}    1.89{col 50}{space 3}0.059{col 58}{space 4}-.0439838{col 71}{space 3} 2.455319
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_chcare_imp   {txt}{c |}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .1020735{col 30}{space 2} .1344495{col 41}{space 1}    0.76{col 50}{space 3}0.448{col 58}{space 4}-.1614427{col 71}{space 3} .3655897
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .2852246{col 30}{space 2} .3747301{col 41}{space 1}    0.76{col 50}{space 3}0.447{col 58}{space 4}-.4492329{col 71}{space 3} 1.019682
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.1337324{col 30}{space 2}  .023342{col 41}{space 1}   -5.73{col 50}{space 3}0.000{col 58}{space 4}-.1794819{col 71}{space 3}-.0879829
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .3509858{col 30}{space 2}  .100529{col 41}{space 1}    3.49{col 50}{space 3}0.000{col 58}{space 4} .1539525{col 71}{space 3} .5480191
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2} .5082826{col 30}{space 2} .1132957{col 41}{space 1}    4.49{col 50}{space 3}0.000{col 58}{space 4} .2862271{col 71}{space 3} .7303381
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0044851{col 30}{space 2}  .009015{col 41}{space 1}   -0.50{col 50}{space 3}0.619{col 58}{space 4}-.0221542{col 71}{space 3}  .013184
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2}-.0674906{col 30}{space 2} .1351759{col 41}{space 1}   -0.50{col 50}{space 3}0.618{col 58}{space 4}-.3324305{col 71}{space 3} .1974493
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.0094352{col 30}{space 2} .1458188{col 41}{space 1}   -0.06{col 50}{space 3}0.948{col 58}{space 4}-.2952347{col 71}{space 3} .2763644
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}  -.00859{col 30}{space 2} .0093791{col 41}{space 1}   -0.92{col 50}{space 3}0.360{col 58}{space 4}-.0269726{col 71}{space 3} .0097926
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.0673419{col 30}{space 2} .0940459{col 41}{space 1}   -0.72{col 50}{space 3}0.474{col 58}{space 4}-.2516685{col 71}{space 3} .1169848
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .3007942{col 30}{space 2} .1054164{col 41}{space 1}    2.85{col 50}{space 3}0.004{col 58}{space 4} .0941817{col 71}{space 3} .5074066
{txt}{space 7}num_child {c |}{col 18}{res}{space 2}-.2332745{col 30}{space 2} .0937141{col 41}{space 1}   -2.49{col 50}{space 3}0.013{col 58}{space 4}-.4169507{col 71}{space 3}-.0495983
{txt}{space 16} {c |}
{space 12}year {c |}
{space 11}2007  {c |}{col 18}{res}{space 2}-.7511333{col 30}{space 2} .1417177{col 41}{space 1}   -5.30{col 50}{space 3}0.000{col 58}{space 4}-1.028895{col 71}{space 3}-.4733717
{txt}{space 16} {c |}
{space 2}ind_same_state {c |}{col 18}{res}{space 2}-.0304849{col 30}{space 2} .0920686{col 41}{space 1}   -0.33{col 50}{space 3}0.741{col 58}{space 4}-.2109361{col 71}{space 3} .1499663
{txt}{space 7}old_child {c |}{col 18}{res}{space 2}-.1278593{col 30}{space 2} .1241522{col 41}{space 1}   -1.03{col 50}{space 3}0.303{col 58}{space 4}-.3711933{col 71}{space 3} .1154746
{txt}{space 1}old_child_2plus {c |}{col 18}{res}{space 2} .1293548{col 30}{space 2} .2170029{col 41}{space 1}    0.60{col 50}{space 3}0.551{col 58}{space 4}-.2959631{col 71}{space 3} .5546728
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 1.042734{col 30}{space 2}  .440024{col 41}{space 1}    2.37{col 50}{space 3}0.018{col 58}{space 4} .1803028{col 71}{space 3} 1.905165
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills           {txt}{c |}
{space 10}lambda {c |}{col 18}{res}{space 2} .8739243{col 30}{space 2} .4598572{col 41}{space 1}    1.90{col 50}{space 3}0.057{col 58}{space 4}-.0273794{col 71}{space 3} 1.775228
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             rho {c |} {res}   0.65023
           {txt}sigma {c |} {res} 1.3440235
{txt}{hline 17}{c BT}{hline 64}
{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}     1,282
{txt}(regression model with sample selection)              Selected    = {res}       302
                                                {txt}      Nonselected = {res}       980

                                                {txt}Wald chi2({res}13{txt})     =  {res}    40.69
                                                {txt}Prob > chi2       =     {res}0.0001

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_invratio_imp  {txt}{c |}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2}  .498517{col 30}{space 2} .2133206{col 41}{space 1}    2.34{col 50}{space 3}0.019{col 58}{space 4} .0804162{col 71}{space 3} .9166178
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .1969441{col 30}{space 2} .6367404{col 41}{space 1}    0.31{col 50}{space 3}0.757{col 58}{space 4}-1.051044{col 71}{space 3} 1.444932
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.1425468{col 30}{space 2} .0694142{col 41}{space 1}   -2.05{col 50}{space 3}0.040{col 58}{space 4} -.278596{col 71}{space 3}-.0064975
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .2256508{col 30}{space 2} .2018854{col 41}{space 1}    1.12{col 50}{space 3}0.264{col 58}{space 4}-.1700372{col 71}{space 3} .6213389
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2} .0225444{col 30}{space 2} .2396864{col 41}{space 1}    0.09{col 50}{space 3}0.925{col 58}{space 4}-.4472323{col 71}{space 3} .4923211
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0036436{col 30}{space 2} .0140647{col 41}{space 1}   -0.26{col 50}{space 3}0.796{col 58}{space 4}-.0312098{col 71}{space 3} .0239226
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .1738628{col 30}{space 2} .2175139{col 41}{space 1}    0.80{col 50}{space 3}0.424{col 58}{space 4}-.2524567{col 71}{space 3} .6001822
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.6933296{col 30}{space 2}  .246343{col 41}{space 1}   -2.81{col 50}{space 3}0.005{col 58}{space 4}-1.176153{col 71}{space 3}-.2105062
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0081469{col 30}{space 2} .0157575{col 41}{space 1}   -0.52{col 50}{space 3}0.605{col 58}{space 4} -.039031{col 71}{space 3} .0227372
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.2467966{col 30}{space 2} .1497424{col 41}{space 1}   -1.65{col 50}{space 3}0.099{col 58}{space 4}-.5402863{col 71}{space 3} .0466932
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .1323851{col 30}{space 2}  .180636{col 41}{space 1}    0.73{col 50}{space 3}0.464{col 58}{space 4} -.221655{col 71}{space 3} .4864251
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} -.003637{col 30}{space 2} .1509104{col 41}{space 1}   -0.02{col 50}{space 3}0.981{col 58}{space 4}-.2994159{col 71}{space 3} .2921419
{txt}{space 6}logtermPHI {c |}{col 18}{res}{space 2} .4548768{col 30}{space 2} .2897722{col 41}{space 1}    1.57{col 50}{space 3}0.116{col 58}{space 4}-.1130663{col 71}{space 3}  1.02282
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} -.306225{col 30}{space 2}  1.13654{col 41}{space 1}   -0.27{col 50}{space 3}0.788{col 58}{space 4}-2.533802{col 71}{space 3} 1.921352
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_chcare_imp   {txt}{c |}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .0873997{col 30}{space 2} .1384667{col 41}{space 1}    0.63{col 50}{space 3}0.528{col 58}{space 4}-.1839901{col 71}{space 3} .3587895
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .1933072{col 30}{space 2} .3874193{col 41}{space 1}    0.50{col 50}{space 3}0.618{col 58}{space 4}-.5660207{col 71}{space 3} .9526351
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.1216834{col 30}{space 2} .0239945{col 41}{space 1}   -5.07{col 50}{space 3}0.000{col 58}{space 4}-.1687117{col 71}{space 3}-.0746551
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .3507454{col 30}{space 2}   .10413{col 41}{space 1}    3.37{col 50}{space 3}0.001{col 58}{space 4} .1466544{col 71}{space 3} .5548364
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}  .550143{col 30}{space 2} .1174515{col 41}{space 1}    4.68{col 50}{space 3}0.000{col 58}{space 4} .3199422{col 71}{space 3} .7803437
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0049129{col 30}{space 2} .0093079{col 41}{space 1}   -0.53{col 50}{space 3}0.598{col 58}{space 4} -.023156{col 71}{space 3} .0133302
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2}-.0974158{col 30}{space 2} .1396259{col 41}{space 1}   -0.70{col 50}{space 3}0.485{col 58}{space 4}-.3710776{col 71}{space 3}  .176246
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.1376703{col 30}{space 2} .1540866{col 41}{space 1}   -0.89{col 50}{space 3}0.372{col 58}{space 4}-.4396745{col 71}{space 3} .1643338
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0063118{col 30}{space 2} .0097108{col 41}{space 1}   -0.65{col 50}{space 3}0.516{col 58}{space 4}-.0253446{col 71}{space 3} .0127211
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.0697085{col 30}{space 2} .0972579{col 41}{space 1}   -0.72{col 50}{space 3}0.474{col 58}{space 4}-.2603305{col 71}{space 3} .1209136
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .2912419{col 30}{space 2} .1088541{col 41}{space 1}    2.68{col 50}{space 3}0.007{col 58}{space 4} .0778917{col 71}{space 3}  .504592
{txt}{space 7}num_child {c |}{col 18}{res}{space 2}-.2464214{col 30}{space 2} .0971691{col 41}{space 1}   -2.54{col 50}{space 3}0.011{col 58}{space 4}-.4368694{col 71}{space 3}-.0559734
{txt}{space 16} {c |}
{space 12}year {c |}
{space 11}2007  {c |}{col 18}{res}{space 2}-.8859171{col 30}{space 2} .1558328{col 41}{space 1}   -5.69{col 50}{space 3}0.000{col 58}{space 4}-1.191344{col 71}{space 3}-.5804905
{txt}{space 16} {c |}
{space 2}ind_same_state {c |}{col 18}{res}{space 2} -.045755{col 30}{space 2} .0956488{col 41}{space 1}   -0.48{col 50}{space 3}0.632{col 58}{space 4}-.2332232{col 71}{space 3} .1417131
{txt}{space 7}old_child {c |}{col 18}{res}{space 2}-.1774399{col 30}{space 2} .1300228{col 41}{space 1}   -1.36{col 50}{space 3}0.172{col 58}{space 4}-.4322799{col 71}{space 3} .0774002
{txt}{space 1}old_child_2plus {c |}{col 18}{res}{space 2} .0906138{col 30}{space 2} .2338037{col 41}{space 1}    0.39{col 50}{space 3}0.698{col 58}{space 4}-.3676331{col 71}{space 3} .5488606
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .9924482{col 30}{space 2} .4557214{col 41}{space 1}    2.18{col 50}{space 3}0.029{col 58}{space 4} .0992506{col 71}{space 3} 1.885646
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills           {txt}{c |}
{space 10}lambda {c |}{col 18}{res}{space 2} .5481601{col 30}{space 2} .4330204{col 41}{space 1}    1.27{col 50}{space 3}0.206{col 58}{space 4}-.3005442{col 71}{space 3} 1.396864
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             rho {c |} {res}   0.46100
           {txt}sigma {c |} {res} 1.1890652
{txt}{hline 17}{c BT}{hline 64}
{res}
{txt}Heckman selection model -- two-step estimates   Number of obs     = {res}       930
{txt}(regression model with sample selection)              Selected    = {res}       302
                                                {txt}      Nonselected = {res}       628

                                                {txt}Wald chi2({res}13{txt})     =  {res}    39.96
                                                {txt}Prob > chi2       =     {res}0.0001

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_invratio_imp  {txt}{c |}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .4564224{col 30}{space 2} .2135826{col 41}{space 1}    2.14{col 50}{space 3}0.033{col 58}{space 4} .0378082{col 71}{space 3} .8750365
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .0847626{col 30}{space 2} .6351111{col 41}{space 1}    0.13{col 50}{space 3}0.894{col 58}{space 4}-1.160032{col 71}{space 3} 1.329557
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.1286034{col 30}{space 2} .0659193{col 41}{space 1}   -1.95{col 50}{space 3}0.051{col 58}{space 4}-.2578029{col 71}{space 3} .0005961
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .1745285{col 30}{space 2} .1866245{col 41}{space 1}    0.94{col 50}{space 3}0.350{col 58}{space 4}-.1912489{col 71}{space 3} .5403059
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.0619583{col 30}{space 2} .2077202{col 41}{space 1}   -0.30{col 50}{space 3}0.765{col 58}{space 4}-.4690824{col 71}{space 3} .3451658
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0018781{col 30}{space 2} .0138257{col 41}{space 1}   -0.14{col 50}{space 3}0.892{col 58}{space 4}-.0289759{col 71}{space 3} .0252198
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .1721309{col 30}{space 2} .2169882{col 41}{space 1}    0.79{col 50}{space 3}0.428{col 58}{space 4}-.2531582{col 71}{space 3}   .59742
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.6603376{col 30}{space 2} .2440902{col 41}{space 1}   -2.71{col 50}{space 3}0.007{col 58}{space 4}-1.138746{col 71}{space 3}-.1819297
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0064136{col 30}{space 2}  .015637{col 41}{space 1}   -0.41{col 50}{space 3}0.682{col 58}{space 4}-.0370616{col 71}{space 3} .0242344
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.2144552{col 30}{space 2}  .149763{col 41}{space 1}   -1.43{col 50}{space 3}0.152{col 58}{space 4}-.5079853{col 71}{space 3} .0790749
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2}  .126007{col 30}{space 2} .1841614{col 41}{space 1}    0.68{col 50}{space 3}0.494{col 58}{space 4}-.2349428{col 71}{space 3} .4869567
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .0096203{col 30}{space 2} .1525166{col 41}{space 1}    0.06{col 50}{space 3}0.950{col 58}{space 4}-.2893068{col 71}{space 3} .3085475
{txt}{space 6}logtermPHI {c |}{col 18}{res}{space 2} .5440999{col 30}{space 2} .3058582{col 41}{space 1}    1.78{col 50}{space 3}0.075{col 58}{space 4}-.0553711{col 71}{space 3} 1.143571
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} -.491308{col 30}{space 2} 1.146149{col 41}{space 1}   -0.43{col 50}{space 3}0.668{col 58}{space 4}-2.737719{col 71}{space 3} 1.755103
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}pos_chcare_imp   {txt}{c |}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} -.084171{col 30}{space 2} .1579818{col 41}{space 1}   -0.53{col 50}{space 3}0.594{col 58}{space 4}-.3938096{col 71}{space 3} .2254675
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2}-.1910836{col 30}{space 2} .4687362{col 41}{space 1}   -0.41{col 50}{space 3}0.684{col 58}{space 4} -1.10979{col 71}{space 3} .7276226
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.1195857{col 30}{space 2} .0344586{col 41}{space 1}   -3.47{col 50}{space 3}0.001{col 58}{space 4}-.1871233{col 71}{space 3}-.0520481
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .2823325{col 30}{space 2}   .11844{col 41}{space 1}    2.38{col 50}{space 3}0.017{col 58}{space 4} .0501944{col 71}{space 3} .5144705
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2} .3977089{col 30}{space 2} .1327822{col 41}{space 1}    3.00{col 50}{space 3}0.003{col 58}{space 4} .1374605{col 71}{space 3} .6579573
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}  .000562{col 30}{space 2} .0107387{col 41}{space 1}    0.05{col 50}{space 3}0.958{col 58}{space 4}-.0204854{col 71}{space 3} .0216094
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2}-.1046787{col 30}{space 2} .1539155{col 41}{space 1}   -0.68{col 50}{space 3}0.496{col 58}{space 4}-.4063475{col 71}{space 3} .1969901
{txt}fed_all_collplus {c |}{col 18}{res}{space 2} -.100029{col 30}{space 2} .1784264{col 41}{space 1}   -0.56{col 50}{space 3}0.575{col 58}{space 4}-.4497385{col 71}{space 3} .2496804
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0013391{col 30}{space 2} .0114649{col 41}{space 1}   -0.12{col 50}{space 3}0.907{col 58}{space 4}-.0238099{col 71}{space 3} .0211316
{txt}{space 9}m_white {c |}{col 18}{res}{space 2} .0261973{col 30}{space 2}  .111203{col 41}{space 1}    0.24{col 50}{space 3}0.814{col 58}{space 4}-.1917565{col 71}{space 3} .2441511
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .3387282{col 30}{space 2} .1289775{col 41}{space 1}    2.63{col 50}{space 3}0.009{col 58}{space 4} .0859369{col 71}{space 3} .5915194
{txt}{space 7}num_child {c |}{col 18}{res}{space 2}-.2444288{col 30}{space 2} .1107481{col 41}{space 1}   -2.21{col 50}{space 3}0.027{col 58}{space 4}-.4614912{col 71}{space 3}-.0273665
{txt}{space 16} {c |}
{space 12}year {c |}
{space 11}2007  {c |}{col 18}{res}{space 2}-.7719564{col 30}{space 2} .1715579{col 41}{space 1}   -4.50{col 50}{space 3}0.000{col 58}{space 4}-1.108204{col 71}{space 3} -.435709
{txt}{space 16} {c |}
{space 6}logtermPHI {c |}{col 18}{res}{space 2} .3215868{col 30}{space 2}  .185736{col 41}{space 1}    1.73{col 50}{space 3}0.083{col 58}{space 4}-.0424491{col 71}{space 3} .6856227
{txt}{space 2}ind_same_state {c |}{col 18}{res}{space 2}-.1487274{col 30}{space 2} .1073453{col 41}{space 1}   -1.39{col 50}{space 3}0.166{col 58}{space 4}-.3591204{col 71}{space 3} .0616656
{txt}{space 7}old_child {c |}{col 18}{res}{space 2}-.3131371{col 30}{space 2} .1445027{col 41}{space 1}   -2.17{col 50}{space 3}0.030{col 58}{space 4}-.5963571{col 71}{space 3}-.0299171
{txt}{space 1}old_child_2plus {c |}{col 18}{res}{space 2}  .115802{col 30}{space 2} .2497528{col 41}{space 1}    0.46{col 50}{space 3}0.643{col 58}{space 4}-.3737044{col 71}{space 3} .6053085
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .5465039{col 30}{space 2} .7738541{col 41}{space 1}    0.71{col 50}{space 3}0.480{col 58}{space 4}-.9702223{col 71}{space 3}  2.06323
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}/mills           {txt}{c |}
{space 10}lambda {c |}{col 18}{res}{space 2} .4670758{col 30}{space 2} .4255596{col 41}{space 1}    1.10{col 50}{space 3}0.272{col 58}{space 4}-.3670057{col 71}{space 3} 1.301157
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             rho {c |} {res}   0.40276
           {txt}sigma {c |} {res} 1.1596786
{txt}{hline 17}{c BT}{hline 64}
{res}{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/All_YPvpg_4ca_OLS_Heckman.tex"'})

      Source {c |}       SS           df       MS      Number of obs   ={res}       186
{txt}{hline 13}{c +}{hline 34}   F(12, 173)      = {res}     2.41
{txt}       Model {c |} {res} 34.9322032        12  2.91101694   {txt}Prob > F        ={res}    0.0065
{txt}    Residual {c |} {res} 208.800262       173  1.20693793   {txt}R-squared       ={res}    0.1433
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0839
{txt}       Total {c |} {res} 243.732465       185  1.31747278   {txt}Root MSE        =   {res} 1.0986

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2}  .332082{col 30}{space 2} .2654046{col 41}{space 1}    1.25{col 50}{space 3}0.213{col 58}{space 4}-.1917661{col 71}{space 3} .8559301
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} 1.033425{col 30}{space 2} .7154651{col 41}{space 1}    1.44{col 50}{space 3}0.150{col 58}{space 4}-.3787392{col 71}{space 3}  2.44559
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.2349284{col 30}{space 2} .0955736{col 41}{space 1}   -2.46{col 50}{space 3}0.015{col 58}{space 4}-.4235689{col 71}{space 3}-.0462879
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .0179739{col 30}{space 2} .2169829{col 41}{space 1}    0.08{col 50}{space 3}0.934{col 58}{space 4}-.4103006{col 71}{space 3} .4462485
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.5243365{col 30}{space 2} .2170365{col 41}{space 1}   -2.42{col 50}{space 3}0.017{col 58}{space 4}-.9527169{col 71}{space 3} -.095956
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0150048{col 30}{space 2} .0185373{col 41}{space 1}    0.81{col 50}{space 3}0.419{col 58}{space 4}-.0215837{col 71}{space 3} .0515932
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .0797182{col 30}{space 2} .2594776{col 41}{space 1}    0.31{col 50}{space 3}0.759{col 58}{space 4}-.4324313{col 71}{space 3} .5918677
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.2135942{col 30}{space 2} .2687608{col 41}{space 1}   -0.79{col 50}{space 3}0.428{col 58}{space 4}-.7440665{col 71}{space 3} .3168782
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0246375{col 30}{space 2} .0193285{col 41}{space 1}   -1.27{col 50}{space 3}0.204{col 58}{space 4}-.0627876{col 71}{space 3} .0135125
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.2255957{col 30}{space 2} .1768215{col 41}{space 1}   -1.28{col 50}{space 3}0.204{col 58}{space 4}-.5746009{col 71}{space 3} .1234096
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .0022423{col 30}{space 2} .1748364{col 41}{space 1}    0.01{col 50}{space 3}0.990{col 58}{space 4}-.3428448{col 71}{space 3} .3473295
{txt}{space 7}num_child {c |}{col 18}{res}{space 2}-.0069846{col 30}{space 2} .1466289{col 41}{space 1}   -0.05{col 50}{space 3}0.962{col 58}{space 4}-.2963964{col 71}{space 3} .2824272
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 1.833538{col 30}{space 2}  .935808{col 41}{space 1}    1.96{col 50}{space 3}0.052{col 58}{space 4}-.0135328{col 71}{space 3} 3.680609
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-.4971897{col 26}{space 2}  .594925{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-1.663221{col 67}{space 3} .6688418
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   173) ={res}    4.52
{txt}{col 13}Prob > F ={res}    0.0122

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   173) ={res}    0.33
{txt}{col 13}Prob > F ={res}    0.7187

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       161
{txt}{hline 13}{c +}{hline 34}   F(12, 148)      = {res}     1.43
{txt}       Model {c |} {res} 26.1569441        12  2.17974534   {txt}Prob > F        ={res}    0.1578
{txt}    Residual {c |} {res} 225.446222       148  1.52328528   {txt}R-squared       ={res}    0.1040
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0313
{txt}       Total {c |} {res} 251.603166       160  1.57251979   {txt}Root MSE        =   {res} 1.2342

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .8736254{col 30}{space 2} .3308323{col 41}{space 1}    2.64{col 50}{space 3}0.009{col 58}{space 4} .2198602{col 71}{space 3} 1.527391
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .5551739{col 30}{space 2} .9815678{col 41}{space 1}    0.57{col 50}{space 3}0.573{col 58}{space 4}-1.384524{col 71}{space 3} 2.494872
{txt}{space 13}age {c |}{col 18}{res}{space 2} .0849973{col 30}{space 2} .0958398{col 41}{space 1}    0.89{col 50}{space 3}0.377{col 58}{space 4} -.104394{col 71}{space 3} .2743885
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2}-.1162174{col 30}{space 2}  .247326{col 41}{space 1}   -0.47{col 50}{space 3}0.639{col 58}{space 4}-.6049639{col 71}{space 3} .3725291
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.0312566{col 30}{space 2} .2788079{col 41}{space 1}   -0.11{col 50}{space 3}0.911{col 58}{space 4}-.5822153{col 71}{space 3}  .519702
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0144617{col 30}{space 2} .0215371{col 41}{space 1}   -0.67{col 50}{space 3}0.503{col 58}{space 4}-.0570217{col 71}{space 3} .0280983
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .1645169{col 30}{space 2} .3496055{col 41}{space 1}    0.47{col 50}{space 3}0.639{col 58}{space 4}-.5263464{col 71}{space 3} .8553802
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.7457601{col 30}{space 2} .3718657{col 41}{space 1}   -2.01{col 50}{space 3}0.047{col 58}{space 4}-1.480612{col 71}{space 3}-.0109079
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0075354{col 30}{space 2}  .024853{col 41}{space 1}   -0.30{col 50}{space 3}0.762{col 58}{space 4}-.0566481{col 71}{space 3} .0415772
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.4442094{col 30}{space 2} .2285973{col 41}{space 1}   -1.94{col 50}{space 3}0.054{col 58}{space 4}-.8959456{col 71}{space 3} .0075268
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2}-.0793165{col 30}{space 2} .2623054{col 41}{space 1}   -0.30{col 50}{space 3}0.763{col 58}{space 4} -.597664{col 71}{space 3}  .439031
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .1767575{col 30}{space 2} .1780522{col 41}{space 1}    0.99{col 50}{space 3}0.322{col 58}{space 4}-.1750955{col 71}{space 3} .5286105
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-1.004984{col 30}{space 2}  1.23537{col 41}{space 1}   -0.81{col 50}{space 3}0.417{col 58}{space 4}-3.446226{col 71}{space 3} 1.436259
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-6.912982{col 26}{space 2} 20.71516{col 37}{space 1}   -0.33{col 46}{space 3}0.739{col 54}{space 4}-47.51395{col 67}{space 3} 33.68798
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   148) ={res}    0.13
{txt}{col 13}Prob > F ={res}    0.8813

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   148) ={res}    2.29
{txt}{col 13}Prob > F ={res}    0.1047

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       130
{txt}{hline 13}{c +}{hline 34}   F(13, 116)      = {res}     4.14
{txt}       Model {c |} {res} 50.3593041        13  3.87379263   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 108.627827       116  .936446789   {txt}R-squared       ={res}    0.3168
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2402
{txt}       Total {c |} {res} 158.987132       129  1.23245838   {txt}Root MSE        =   {res}  .9677

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .3718966{col 30}{space 2} .2879746{col 41}{space 1}    1.29{col 50}{space 3}0.199{col 58}{space 4}-.1984734{col 71}{space 3} .9422665
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .9332497{col 30}{space 2} .8239973{col 41}{space 1}    1.13{col 50}{space 3}0.260{col 58}{space 4}-.6987809{col 71}{space 3}  2.56528
{txt}{space 13}age {c |}{col 18}{res}{space 2} .0226172{col 30}{space 2} .1087227{col 41}{space 1}    0.21{col 50}{space 3}0.836{col 58}{space 4} -.192722{col 71}{space 3} .2379563
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2}  .109149{col 30}{space 2} .2324633{col 41}{space 1}    0.47{col 50}{space 3}0.640{col 58}{space 4}-.3512739{col 71}{space 3}  .569572
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.2131077{col 30}{space 2} .2356619{col 41}{space 1}   -0.90{col 50}{space 3}0.368{col 58}{space 4}-.6798659{col 71}{space 3} .2536504
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0044522{col 30}{space 2} .0210933{col 41}{space 1}    0.21{col 50}{space 3}0.833{col 58}{space 4}-.0373257{col 71}{space 3} .0462301
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2}-.1165288{col 30}{space 2} .2825167{col 41}{space 1}   -0.41{col 50}{space 3}0.681{col 58}{space 4}-.6760888{col 71}{space 3} .4430312
{txt}fed_all_collplus {c |}{col 18}{res}{space 2} -.416503{col 30}{space 2} .2925735{col 41}{space 1}   -1.42{col 50}{space 3}0.157{col 58}{space 4}-.9959818{col 71}{space 3} .1629757
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}-.0211635{col 30}{space 2} .0220307{col 41}{space 1}   -0.96{col 50}{space 3}0.339{col 58}{space 4} -.064798{col 71}{space 3} .0224711
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.2023608{col 30}{space 2} .1849787{col 41}{space 1}   -1.09{col 50}{space 3}0.276{col 58}{space 4}-.5687343{col 71}{space 3} .1640127
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .3701502{col 30}{space 2} .1936605{col 41}{space 1}    1.91{col 50}{space 3}0.058{col 58}{space 4}-.0134189{col 71}{space 3} .7537192
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .0325499{col 30}{space 2} .1491602{col 41}{space 1}    0.22{col 50}{space 3}0.828{col 58}{space 4}-.2628806{col 71}{space 3} .3279805
{txt}{space 9}logterm {c |}{col 18}{res}{space 2}  .470578{col 30}{space 2} .0983451{col 41}{space 1}    4.78{col 50}{space 3}0.000{col 58}{space 4} .2757931{col 71}{space 3} .6653629
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-1.307143{col 30}{space 2} 1.169166{col 41}{space 1}   -1.12{col 50}{space 3}0.266{col 58}{space 4}-3.622823{col 71}{space 3} 1.008537
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-.5920945{col 26}{space 2} .7299479{col 37}{space 1}   -0.81{col 46}{space 3}0.417{col 54}{space 4}-2.022766{col 67}{space 3}  .838577
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   116) ={res}    1.12
{txt}{col 13}Prob > F ={res}    0.3284

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   116) ={res}    1.98
{txt}{col 13}Prob > F ={res}    0.1428

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       119
{txt}{hline 13}{c +}{hline 34}   F(13, 105)      = {res}     2.85
{txt}       Model {c |} {res} 47.7040963        13  3.66954587   {txt}Prob > F        ={res}    0.0015
{txt}    Residual {c |} {res} 135.248326       105  1.28807929   {txt}R-squared       ={res}    0.2607
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1692
{txt}       Total {c |} {res} 182.952422       118  1.55044426   {txt}Root MSE        =   {res} 1.1349

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .6374789{col 30}{space 2} .3494687{col 41}{space 1}    1.82{col 50}{space 3}0.071{col 58}{space 4} -.055453{col 71}{space 3} 1.330411
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2}-1.348151{col 30}{space 2} 1.104897{col 41}{space 1}   -1.22{col 50}{space 3}0.225{col 58}{space 4}-3.538958{col 71}{space 3} .8426554
{txt}{space 13}age {c |}{col 18}{res}{space 2} .0726132{col 30}{space 2} .1088106{col 41}{space 1}    0.67{col 50}{space 3}0.506{col 58}{space 4}-.1431382{col 71}{space 3} .2883646
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .0983354{col 30}{space 2} .2757426{col 41}{space 1}    0.36{col 50}{space 3}0.722{col 58}{space 4}-.4484113{col 71}{space 3}  .645082
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2} .1681922{col 30}{space 2} .3023266{col 41}{space 1}    0.56{col 50}{space 3}0.579{col 58}{space 4}-.4312655{col 71}{space 3} .7676499
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0221515{col 30}{space 2} .0237916{col 41}{space 1}   -0.93{col 50}{space 3}0.354{col 58}{space 4}-.0693258{col 71}{space 3} .0250229
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .2648525{col 30}{space 2} .3921538{col 41}{space 1}    0.68{col 50}{space 3}0.501{col 58}{space 4}-.5127161{col 71}{space 3} 1.042421
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-1.100117{col 30}{space 2} .4224396{col 41}{space 1}   -2.60{col 50}{space 3}0.011{col 58}{space 4}-1.937737{col 71}{space 3}-.2624978
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2} .0285142{col 30}{space 2} .0276055{col 41}{space 1}    1.03{col 50}{space 3}0.304{col 58}{space 4}-.0262223{col 71}{space 3} .0832507
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.4218493{col 30}{space 2} .2422866{col 41}{space 1}   -1.74{col 50}{space 3}0.085{col 58}{space 4}-.9022588{col 71}{space 3} .0585602
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .0740486{col 30}{space 2} .2884944{col 41}{space 1}    0.26{col 50}{space 3}0.798{col 58}{space 4}-.4979824{col 71}{space 3} .6460797
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .2068767{col 30}{space 2} .1882329{col 41}{space 1}    1.10{col 50}{space 3}0.274{col 58}{space 4}-.1663544{col 71}{space 3} .5801079
{txt}{space 9}logterm {c |}{col 18}{res}{space 2} .4737595{col 30}{space 2} .1276838{col 41}{space 1}    3.71{col 50}{space 3}0.000{col 58}{space 4} .2205862{col 71}{space 3} .7269329
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} -1.65868{col 30}{space 2} 1.341272{col 41}{space 1}   -1.24{col 50}{space 3}0.219{col 58}{space 4}-4.318174{col 71}{space 3} 1.000815
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2} -1.75846{col 26}{space 2} 2.659143{col 37}{space 1}   -0.66{col 46}{space 3}0.508{col 54}{space 4}-6.970284{col 67}{space 3} 3.453364
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   105) ={res}    0.16
{txt}{col 13}Prob > F ={res}    0.8562

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   105) ={res}    3.87
{txt}{col 13}Prob > F ={res}    0.0238

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       163
{txt}{hline 13}{c +}{hline 34}   F(13, 149)      = {res}     1.61
{txt}       Model {c |} {res} 23.8613565        13  1.83548896   {txt}Prob > F        ={res}    0.0887
{txt}    Residual {c |} {res} 170.074884       149  1.14144218   {txt}R-squared       ={res}    0.1230
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0465
{txt}       Total {c |} {res} 193.936241       162  1.19713729   {txt}Root MSE        =   {res} 1.0684

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .1558332{col 30}{space 2} .2797549{col 41}{space 1}    0.56{col 50}{space 3}0.578{col 58}{space 4}-.3969662{col 71}{space 3} .7086326
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .1726159{col 30}{space 2} .7773194{col 41}{space 1}    0.22{col 50}{space 3}0.825{col 58}{space 4}-1.363377{col 71}{space 3} 1.708609
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.1347395{col 30}{space 2} .1102328{col 41}{space 1}   -1.22{col 50}{space 3}0.224{col 58}{space 4}-.3525609{col 71}{space 3}  .083082
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .0045459{col 30}{space 2} .2256997{col 41}{space 1}    0.02{col 50}{space 3}0.984{col 58}{space 4}-.4414396{col 71}{space 3} .4505315
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}-.4230448{col 30}{space 2} .2237573{col 41}{space 1}   -1.89{col 50}{space 3}0.061{col 58}{space 4}-.8651921{col 71}{space 3} .0191025
{txt}{space 11}m_age {c |}{col 18}{res}{space 2} .0065422{col 30}{space 2} .0190351{col 41}{space 1}    0.34{col 50}{space 3}0.732{col 58}{space 4}-.0310714{col 71}{space 3} .0441559
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .1731754{col 30}{space 2} .2678697{col 41}{space 1}    0.65{col 50}{space 3}0.519{col 58}{space 4}-.3561386{col 71}{space 3} .7024894
{txt}fed_all_collplus {c |}{col 18}{res}{space 2} -.430856{col 30}{space 2} .3070583{col 41}{space 1}   -1.40{col 50}{space 3}0.163{col 58}{space 4}-1.037607{col 71}{space 3} .1758952
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2}  -.00769{col 30}{space 2} .0200921{col 41}{space 1}   -0.38{col 50}{space 3}0.702{col 58}{space 4}-.0473922{col 71}{space 3} .0320123
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.0537019{col 30}{space 2} .1879629{col 41}{space 1}   -0.29{col 50}{space 3}0.776{col 58}{space 4}-.4251191{col 71}{space 3} .3177153
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .0520119{col 30}{space 2} .1909723{col 41}{space 1}    0.27{col 50}{space 3}0.786{col 58}{space 4}-.3253518{col 71}{space 3} .4293756
{txt}{space 7}num_child {c |}{col 18}{res}{space 2}-.0774033{col 30}{space 2} .1538642{col 41}{space 1}   -0.50{col 50}{space 3}0.616{col 58}{space 4} -.381441{col 71}{space 3} .2266345
{txt}{space 6}logtermPHI {c |}{col 18}{res}{space 2} .5817969{col 30}{space 2} .3711755{col 41}{space 1}    1.57{col 50}{space 3}0.119{col 58}{space 4}-.1516508{col 71}{space 3} 1.315245
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .0368277{col 30}{space 2} 1.574411{col 41}{space 1}    0.02{col 50}{space 3}0.981{col 58}{space 4} -3.07423{col 71}{space 3} 3.147885
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}   -.1846{col 26}{space 2} .3925736{col 37}{space 1}   -0.47{col 46}{space 3}0.638{col 54}{space 4}-.9540302{col 67}{space 3} .5848303
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   149) ={res}    2.65
{txt}{col 13}Prob > F ={res}    0.0739

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   149) ={res}    1.00
{txt}{col 13}Prob > F ={res}    0.3720

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       147
{txt}{hline 13}{c +}{hline 34}   F(13, 133)      = {res}     1.36
{txt}       Model {c |} {res} 26.1437613        13  2.01105856   {txt}Prob > F        ={res}    0.1856
{txt}    Residual {c |} {res} 196.241587       133  1.47550065   {txt}R-squared       ={res}    0.1176
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0313
{txt}       Total {c |} {res} 222.385348       146  1.52318732   {txt}Root MSE        =   {res} 1.2147

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} ln_invratio_imp{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ln_pratio_4ca {c |}{col 18}{res}{space 2} .8485344{col 30}{space 2} .3391261{col 41}{space 1}    2.50{col 50}{space 3}0.014{col 58}{space 4} .1777562{col 71}{space 3} 1.519313
{txt}{space 4}curr_married {c |}{col 18}{res}{space 2} .5091142{col 30}{space 2} 1.059708{col 41}{space 1}    0.48{col 50}{space 3}0.632{col 58}{space 4}-1.586948{col 71}{space 3} 2.605176
{txt}{space 13}age {c |}{col 18}{res}{space 2} .0537766{col 30}{space 2} .1141409{col 41}{space 1}    0.47{col 50}{space 3}0.638{col 58}{space 4}-.1719897{col 71}{space 3} .2795429
{txt}{space 7}med_scoll {c |}{col 18}{res}{space 2} .0156037{col 30}{space 2} .2642449{col 41}{space 1}    0.06{col 50}{space 3}0.953{col 58}{space 4}-.5070625{col 71}{space 3} .5382698
{txt}{space 4}med_collplus {c |}{col 18}{res}{space 2}   .06048{col 30}{space 2} .3060217{col 41}{space 1}    0.20{col 50}{space 3}0.844{col 58}{space 4} -.544819{col 71}{space 3}  .665779
{txt}{space 11}m_age {c |}{col 18}{res}{space 2}-.0097224{col 30}{space 2} .0227058{col 41}{space 1}   -0.43{col 50}{space 3}0.669{col 58}{space 4}-.0546337{col 71}{space 3} .0351888
{txt}{space 3}fed_all_scoll {c |}{col 18}{res}{space 2} .2214772{col 30}{space 2} .3559462{col 41}{space 1}    0.62{col 50}{space 3}0.535{col 58}{space 4}-.4825705{col 71}{space 3}  .925525
{txt}fed_all_collplus {c |}{col 18}{res}{space 2}-.8089256{col 30}{space 2} .4034006{col 41}{space 1}   -2.01{col 50}{space 3}0.047{col 58}{space 4}-1.606836{col 71}{space 3}-.0110149
{txt}{space 7}f_all_age {c |}{col 18}{res}{space 2} -.009548{col 30}{space 2} .0256038{col 41}{space 1}   -0.37{col 50}{space 3}0.710{col 58}{space 4}-.0601914{col 71}{space 3} .0410954
{txt}{space 9}m_white {c |}{col 18}{res}{space 2}-.4729028{col 30}{space 2} .2365229{col 41}{space 1}   -2.00{col 50}{space 3}0.048{col 58}{space 4}-.9407359{col 71}{space 3}-.0050696
{txt}{space 9}num_0_5 {c |}{col 18}{res}{space 2} .0099636{col 30}{space 2} .2915351{col 41}{space 1}    0.03{col 50}{space 3}0.973{col 58}{space 4}-.5666815{col 71}{space 3} .5866087
{txt}{space 7}num_child {c |}{col 18}{res}{space 2} .2616309{col 30}{space 2}  .185623{col 41}{space 1}    1.41{col 50}{space 3}0.161{col 58}{space 4}-.1055242{col 71}{space 3} .6287861
{txt}{space 6}logtermPHI {c |}{col 18}{res}{space 2} .0201351{col 30}{space 2} .4922205{col 41}{space 1}    0.04{col 50}{space 3}0.967{col 58}{space 4}-.9534579{col 71}{space 3} .9937281
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-1.045002{col 30}{space 2} 2.007489{col 41}{space 1}   -0.52{col 50}{space 3}0.604{col 58}{space 4}-5.015737{col 71}{space 3} 2.925732
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
       {txt}gamma:  {res}_b[ln_pratio_4ca]/(_b[ln_pratio_4ca] -1.0)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ln_invrati~p{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gamma {c |}{col 14}{res}{space 2}-5.602161{col 26}{space 2} 14.78201{col 37}{space 1}   -0.38{col 46}{space 3}0.705{col 54}{space 4}-34.57437{col 67}{space 3} 23.37004
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}med_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} med_collplus = 0{p_end}

{txt}       F(  2,   133) ={res}    0.02
{txt}{col 13}Prob > F ={res}    0.9780

{p 0 7}{space 1}{text:( 1)}{space 1} fed_all_scoll = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} fed_all_collplus = 0{p_end}

{txt}       F(  2,   133) ={res}    2.10
{txt}{col 13}Prob > F ={res}    0.1260
{txt}(output written to {browse  `"D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\output\tables/All_YPvpg_4ca_OLS_age.tex"'})
{com}. 
. 
. {c )-} // end of $est_chcare_goods=1 case
{txt}
{com}. 
. 
. 
. log close mainlog
      {txt}name:  {res}mainlog
       {txt}log:  {res}D:\users\llochner\Dropbox\JOINT PROJECTS\JPE_Child_Devp_2020\FINAL_Replication_CLMP_JPE\code\estimation\basics_rel_demand\logs/5_demand_estimation.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 9 Jul 2025, 17:16:07
{txt}{.-}
{smcl}
{txt}{sf}{ul off}